{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 第一期 Pandas基础"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1.将下面的字典创建为DataFrame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = {\"grammer\":[\"Python\",\"C\",\"Java\",\"GO\",\"R\",\"SQL\",\"PHP\",\"Python\"],\n",
    "       \"score\":[1,2,np.nan,4,5,6,7,10]}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>grammer</th>\n",
       "      <th>score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Python</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>C</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Java</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>GO</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>R</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>SQL</td>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>PHP</td>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Python</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  grammer  score\n",
       "0  Python    1.0\n",
       "1       C    2.0\n",
       "2    Java    NaN\n",
       "3      GO    4.0\n",
       "4       R    5.0\n",
       "5     SQL    6.0\n",
       "6     PHP    7.0\n",
       "7  Python   10.0"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.提取含有字符串\"Python\"的行"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  grammer  score\n",
      "0  Python    1.0\n",
      "7  Python   10.0\n"
     ]
    }
   ],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.输出df的所有列名"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Index(['grammer', 'score'], dtype='object')\n"
     ]
    }
   ],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4.修改第二列列名为'popularity'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>grammer</th>\n",
       "      <th>popularity</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Python</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>C</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Java</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>GO</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>R</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>SQL</td>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>PHP</td>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Python</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  grammer  popularity\n",
       "0  Python         1.0\n",
       "1       C         2.0\n",
       "2    Java         NaN\n",
       "3      GO         4.0\n",
       "4       R         5.0\n",
       "5     SQL         6.0\n",
       "6     PHP         7.0\n",
       "7  Python        10.0"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 5.统计grammer列中每种编程语言出现的次数"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Python    2\n",
       "Java      1\n",
       "GO        1\n",
       "SQL       1\n",
       "PHP       1\n",
       "R         1\n",
       "C         1\n",
       "Name: grammer, dtype: int64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 6.将空值用上下值的平均值填充"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>grammer</th>\n",
       "      <th>popularity</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Python</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>C</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Java</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>GO</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>R</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>SQL</td>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>PHP</td>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Python</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  grammer  popularity\n",
       "0  Python         1.0\n",
       "1       C         2.0\n",
       "2    Java         3.0\n",
       "3      GO         4.0\n",
       "4       R         5.0\n",
       "5     SQL         6.0\n",
       "6     PHP         7.0\n",
       "7  Python        10.0"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 7.提取popularity列中值大于3的行"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>grammer</th>\n",
       "      <th>popularity</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>GO</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>R</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>SQL</td>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>PHP</td>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Python</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  grammer  popularity\n",
       "3      GO         4.0\n",
       "4       R         5.0\n",
       "5     SQL         6.0\n",
       "6     PHP         7.0\n",
       "7  Python        10.0"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 8.按照grammer列进行去除重复值"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>grammer</th>\n",
       "      <th>popularity</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Python</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>C</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Java</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>GO</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>R</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>SQL</td>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>PHP</td>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  grammer  popularity\n",
       "0  Python         1.0\n",
       "1       C         2.0\n",
       "2    Java         3.0\n",
       "3      GO         4.0\n",
       "4       R         5.0\n",
       "5     SQL         6.0\n",
       "6     PHP         7.0"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 9.计算popularity列平均值"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4.75"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 10.将grammer列转换为list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Python', 'C', 'Java', 'GO', 'R', 'SQL', 'PHP', 'Python']"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 11.将DataFrame保存为EXCEL"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 12.查看数据行列数"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(8, 2)"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 13.提取popularity列值大于3小于7的行"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>grammer</th>\n",
       "      <th>popularity</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>GO</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>R</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>SQL</td>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  grammer  popularity\n",
       "3      GO         4.0\n",
       "4       R         5.0\n",
       "5     SQL         6.0"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 14.交换两列位置"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'\\n方法2\\ncols = df.columns[[1,0]]\\ndf = df[cols]\\ndf\\n'"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 15.提取popularity列最大值所在行"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>popularity</th>\n",
       "      <th>grammer</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.0</td>\n",
       "      <td>Python</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2.0</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3.0</td>\n",
       "      <td>Java</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4.0</td>\n",
       "      <td>GO</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5.0</td>\n",
       "      <td>R</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>6.0</td>\n",
       "      <td>SQL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>7.0</td>\n",
       "      <td>PHP</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>10.0</td>\n",
       "      <td>Python</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   popularity grammer\n",
       "0         1.0  Python\n",
       "1         2.0       C\n",
       "2         3.0    Java\n",
       "3         4.0      GO\n",
       "4         5.0       R\n",
       "5         6.0     SQL\n",
       "6         7.0     PHP\n",
       "7        10.0  Python"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 16.查看最后5行数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>popularity</th>\n",
       "      <th>grammer</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4.0</td>\n",
       "      <td>GO</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5.0</td>\n",
       "      <td>R</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>6.0</td>\n",
       "      <td>SQL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>7.0</td>\n",
       "      <td>PHP</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>10.0</td>\n",
       "      <td>Python</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   popularity grammer\n",
       "3         4.0      GO\n",
       "4         5.0       R\n",
       "5         6.0     SQL\n",
       "6         7.0     PHP\n",
       "7        10.0  Python"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 17.删除最后一行数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>popularity</th>\n",
       "      <th>grammer</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.0</td>\n",
       "      <td>Python</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2.0</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3.0</td>\n",
       "      <td>Java</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4.0</td>\n",
       "      <td>GO</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5.0</td>\n",
       "      <td>R</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>6.0</td>\n",
       "      <td>SQL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>7.0</td>\n",
       "      <td>PHP</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   popularity grammer\n",
       "0         1.0  Python\n",
       "1         2.0       C\n",
       "2         3.0    Java\n",
       "3         4.0      GO\n",
       "4         5.0       R\n",
       "5         6.0     SQL\n",
       "6         7.0     PHP"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 18.添加一行数据['Perl',6.6] "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>popularity</th>\n",
       "      <th>grammer</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.0</td>\n",
       "      <td>Python</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2.0</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3.0</td>\n",
       "      <td>Java</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4.0</td>\n",
       "      <td>GO</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5.0</td>\n",
       "      <td>R</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>6.0</td>\n",
       "      <td>SQL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>7.0</td>\n",
       "      <td>PHP</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>6.6</td>\n",
       "      <td>Perl</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   popularity grammer\n",
       "0         1.0  Python\n",
       "1         2.0       C\n",
       "2         3.0    Java\n",
       "3         4.0      GO\n",
       "4         5.0       R\n",
       "5         6.0     SQL\n",
       "6         7.0     PHP\n",
       "7         6.6    Perl"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 19.对数据按照\"popularity\"列值的大小进行排序"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>popularity</th>\n",
       "      <th>grammer</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.0</td>\n",
       "      <td>Python</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2.0</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3.0</td>\n",
       "      <td>Java</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4.0</td>\n",
       "      <td>GO</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5.0</td>\n",
       "      <td>R</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>6.0</td>\n",
       "      <td>SQL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>6.6</td>\n",
       "      <td>Perl</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>7.0</td>\n",
       "      <td>PHP</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   popularity grammer\n",
       "0         1.0  Python\n",
       "1         2.0       C\n",
       "2         3.0    Java\n",
       "3         4.0      GO\n",
       "4         5.0       R\n",
       "5         6.0     SQL\n",
       "7         6.6    Perl\n",
       "6         7.0     PHP"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 20.统计grammer列每个字符串的长度"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    6\n",
       "1    1\n",
       "2    4\n",
       "3    2\n",
       "4    1\n",
       "5    3\n",
       "7    4\n",
       "6    3\n",
       "Name: grammer, dtype: int64"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 第二期 Pandas数据处理"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 21.读取本地EXCEL数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 22.查看df数据前5行"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>createTime</th>\n",
       "      <th>education</th>\n",
       "      <th>salary</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2020-03-16 11:30:18</td>\n",
       "      <td>本科</td>\n",
       "      <td>20k-35k</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2020-03-16 10:58:48</td>\n",
       "      <td>本科</td>\n",
       "      <td>20k-40k</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2020-03-16 10:46:39</td>\n",
       "      <td>不限</td>\n",
       "      <td>20k-35k</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2020-03-16 10:45:44</td>\n",
       "      <td>本科</td>\n",
       "      <td>13k-20k</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2020-03-16 10:20:41</td>\n",
       "      <td>本科</td>\n",
       "      <td>10k-20k</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           createTime education   salary\n",
       "0 2020-03-16 11:30:18        本科  20k-35k\n",
       "1 2020-03-16 10:58:48        本科  20k-40k\n",
       "2 2020-03-16 10:46:39        不限  20k-35k\n",
       "3 2020-03-16 10:45:44        本科  13k-20k\n",
       "4 2020-03-16 10:20:41        本科  10k-20k"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 23.将salary列数据转换为最大值与最小值的平均值"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>createTime</th>\n",
       "      <th>education</th>\n",
       "      <th>salary</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2020-03-16 11:30:18</td>\n",
       "      <td>本科</td>\n",
       "      <td>27500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2020-03-16 10:58:48</td>\n",
       "      <td>本科</td>\n",
       "      <td>30000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2020-03-16 10:46:39</td>\n",
       "      <td>不限</td>\n",
       "      <td>27500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2020-03-16 10:45:44</td>\n",
       "      <td>本科</td>\n",
       "      <td>16500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2020-03-16 10:20:41</td>\n",
       "      <td>本科</td>\n",
       "      <td>15000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>130</th>\n",
       "      <td>2020-03-16 11:36:07</td>\n",
       "      <td>本科</td>\n",
       "      <td>14000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>131</th>\n",
       "      <td>2020-03-16 09:54:47</td>\n",
       "      <td>硕士</td>\n",
       "      <td>37500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>132</th>\n",
       "      <td>2020-03-16 10:48:32</td>\n",
       "      <td>本科</td>\n",
       "      <td>30000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>133</th>\n",
       "      <td>2020-03-16 10:46:31</td>\n",
       "      <td>本科</td>\n",
       "      <td>19000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>134</th>\n",
       "      <td>2020-03-16 11:19:38</td>\n",
       "      <td>本科</td>\n",
       "      <td>30000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>135 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             createTime education salary\n",
       "0   2020-03-16 11:30:18        本科  27500\n",
       "1   2020-03-16 10:58:48        本科  30000\n",
       "2   2020-03-16 10:46:39        不限  27500\n",
       "3   2020-03-16 10:45:44        本科  16500\n",
       "4   2020-03-16 10:20:41        本科  15000\n",
       "..                  ...       ...    ...\n",
       "130 2020-03-16 11:36:07        本科  14000\n",
       "131 2020-03-16 09:54:47        硕士  37500\n",
       "132 2020-03-16 10:48:32        本科  30000\n",
       "133 2020-03-16 10:46:31        本科  19000\n",
       "134 2020-03-16 11:19:38        本科  30000\n",
       "\n",
       "[135 rows x 3 columns]"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 24.将数据根据学历进行分组并计算最高薪资"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "education\n",
       "不限    30000.0\n",
       "大专    15000.0\n",
       "本科    45000.0\n",
       "硕士    37500.0\n",
       "Name: salary, dtype: float64"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 25.将createTime列时间转换为月-日"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>createTime</th>\n",
       "      <th>education</th>\n",
       "      <th>salary</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>03-16</td>\n",
       "      <td>本科</td>\n",
       "      <td>27500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>03-16</td>\n",
       "      <td>本科</td>\n",
       "      <td>30000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>03-16</td>\n",
       "      <td>不限</td>\n",
       "      <td>27500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>03-16</td>\n",
       "      <td>本科</td>\n",
       "      <td>16500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>03-16</td>\n",
       "      <td>本科</td>\n",
       "      <td>15000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  createTime education salary\n",
       "0      03-16        本科  27500\n",
       "1      03-16        本科  30000\n",
       "2      03-16        不限  27500\n",
       "3      03-16        本科  16500\n",
       "4      03-16        本科  15000"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 26.查看索引、数据类型和内存信息"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 135 entries, 0 to 134\n",
      "Data columns (total 3 columns):\n",
      " #   Column      Non-Null Count  Dtype \n",
      "---  ------      --------------  ----- \n",
      " 0   createTime  135 non-null    object\n",
      " 1   education   135 non-null    object\n",
      " 2   salary      135 non-null    object\n",
      "dtypes: object(3)\n",
      "memory usage: 3.3+ KB\n"
     ]
    }
   ],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 27.查看数值型列的汇总统计"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>createTime</th>\n",
       "      <th>education</th>\n",
       "      <th>salary</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>135</td>\n",
       "      <td>135</td>\n",
       "      <td>135.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>unique</th>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>36.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>top</th>\n",
       "      <td>03-16</td>\n",
       "      <td>本科</td>\n",
       "      <td>30000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>freq</th>\n",
       "      <td>133</td>\n",
       "      <td>119</td>\n",
       "      <td>19.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       createTime education   salary\n",
       "count         135       135    135.0\n",
       "unique          3         4     36.0\n",
       "top         03-16        本科  30000.0\n",
       "freq          133       119     19.0"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 28.新增一列根据salary将数据分为三组"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>createTime</th>\n",
       "      <th>education</th>\n",
       "      <th>salary</th>\n",
       "      <th>categories</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>03-16</td>\n",
       "      <td>本科</td>\n",
       "      <td>27500</td>\n",
       "      <td>高</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>03-16</td>\n",
       "      <td>本科</td>\n",
       "      <td>30000</td>\n",
       "      <td>高</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>03-16</td>\n",
       "      <td>不限</td>\n",
       "      <td>27500</td>\n",
       "      <td>高</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>03-16</td>\n",
       "      <td>本科</td>\n",
       "      <td>16500</td>\n",
       "      <td>中</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>03-16</td>\n",
       "      <td>本科</td>\n",
       "      <td>15000</td>\n",
       "      <td>中</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>130</th>\n",
       "      <td>03-16</td>\n",
       "      <td>本科</td>\n",
       "      <td>14000</td>\n",
       "      <td>中</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>131</th>\n",
       "      <td>03-16</td>\n",
       "      <td>硕士</td>\n",
       "      <td>37500</td>\n",
       "      <td>高</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>132</th>\n",
       "      <td>03-16</td>\n",
       "      <td>本科</td>\n",
       "      <td>30000</td>\n",
       "      <td>高</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>133</th>\n",
       "      <td>03-16</td>\n",
       "      <td>本科</td>\n",
       "      <td>19000</td>\n",
       "      <td>中</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>134</th>\n",
       "      <td>03-16</td>\n",
       "      <td>本科</td>\n",
       "      <td>30000</td>\n",
       "      <td>高</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>135 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    createTime education salary categories\n",
       "0        03-16        本科  27500          高\n",
       "1        03-16        本科  30000          高\n",
       "2        03-16        不限  27500          高\n",
       "3        03-16        本科  16500          中\n",
       "4        03-16        本科  15000          中\n",
       "..         ...       ...    ...        ...\n",
       "130      03-16        本科  14000          中\n",
       "131      03-16        硕士  37500          高\n",
       "132      03-16        本科  30000          高\n",
       "133      03-16        本科  19000          中\n",
       "134      03-16        本科  30000          高\n",
       "\n",
       "[135 rows x 4 columns]"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 29.按照salary列对数据降序排列"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>createTime</th>\n",
       "      <th>education</th>\n",
       "      <th>salary</th>\n",
       "      <th>categories</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>53</th>\n",
       "      <td>03-16</td>\n",
       "      <td>本科</td>\n",
       "      <td>45000</td>\n",
       "      <td>高</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>03-16</td>\n",
       "      <td>本科</td>\n",
       "      <td>40000</td>\n",
       "      <td>高</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>101</th>\n",
       "      <td>03-16</td>\n",
       "      <td>本科</td>\n",
       "      <td>37500</td>\n",
       "      <td>高</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>03-16</td>\n",
       "      <td>本科</td>\n",
       "      <td>37500</td>\n",
       "      <td>高</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>131</th>\n",
       "      <td>03-16</td>\n",
       "      <td>硕士</td>\n",
       "      <td>37500</td>\n",
       "      <td>高</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>123</th>\n",
       "      <td>03-16</td>\n",
       "      <td>本科</td>\n",
       "      <td>4500</td>\n",
       "      <td>低</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>126</th>\n",
       "      <td>03-16</td>\n",
       "      <td>本科</td>\n",
       "      <td>4000</td>\n",
       "      <td>低</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>110</th>\n",
       "      <td>03-16</td>\n",
       "      <td>本科</td>\n",
       "      <td>4000</td>\n",
       "      <td>低</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96</th>\n",
       "      <td>03-16</td>\n",
       "      <td>不限</td>\n",
       "      <td>3500</td>\n",
       "      <td>低</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>113</th>\n",
       "      <td>03-16</td>\n",
       "      <td>本科</td>\n",
       "      <td>3500</td>\n",
       "      <td>低</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>135 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    createTime education salary categories\n",
       "53       03-16        本科  45000          高\n",
       "37       03-16        本科  40000          高\n",
       "101      03-16        本科  37500          高\n",
       "16       03-16        本科  37500          高\n",
       "131      03-16        硕士  37500          高\n",
       "..         ...       ...    ...        ...\n",
       "123      03-16        本科   4500          低\n",
       "126      03-16        本科   4000          低\n",
       "110      03-16        本科   4000          低\n",
       "96       03-16        不限   3500          低\n",
       "113      03-16        本科   3500          低\n",
       "\n",
       "[135 rows x 4 columns]"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 30.取出第33行数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "createTime    03-16\n",
       "education        硕士\n",
       "salary        22500\n",
       "categories        高\n",
       "Name: 32, dtype: object"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 31.计算salary列的中位数"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "17500.0"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 32.绘制薪资水平频率分布直方图"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7ffb075ec1d0>"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 33.绘制薪资水平密度曲线"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7ffb07b73bd0>"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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BzCwdwDlX4lm6AGk41M62PQeYWTTY7ygRN3vMYKaMzOVRNTOJSA99LRA/7mXbe5EMEnRrwp24J8dhgTAzrj9lLGsqG1i1q97vOCISEEctEGY20szmAplmNtvM5oRv5xBqbkoYqytCvzjjsYkJ4Oq5ReRmpPDAonK/o4hIQKQc4/mLCXVMFwF399jeBHzXo0yBtHpXPeOHZZOXeaSLtmJbTnoKnzplLA8sKmfXvoOMGZJQ9V9EenHUMwjn3G+dc+cCn3POndvjdoVz7qkoZQyE9ysamBmnZw/dPregmCQzHnxnm99RRCQAjnoGYWY3OuceBorN7GuHP++cu7uXt8Wd2sYWahpb4rKDuqfCvEwuP3kUf1y2izvOn0ReVnyeLYlI3xyrkzo7/DMHyO3llhBWf9BBHd9nEAC3njWBg22d6osQkaOfQTjn7gv/jIvFfvpr9a56kpMsrqbYOJKphYO4bGYhD76zjc8tKGZYTrrfkUTEJ30dKPcfZjbIzFLN7DUzqzOzG70OFxSrK+qZNCI35qf47quvXTiJlvZO7n19q99RRMRHfR0HcZFzrhH4GKG5mE4A/tmrUEHS2eVYtbOeOWPju/+hp4kFOVwzt4iHF+9gS23zsd8gInGprwWiuynqMuBx51zCTP25saaJptYOSorz/Y4SVf988RQyUpP43tNrNBW4SILqa4F41sw2AHOB18ysAEiIZciW79gHQMm4IT4nia6C3HS+dekUFpfv48kVWpZUJBH1dbrvbwOnAyXOuXbgAHCll8GConTHfobnplOUn+l3lKi7ft5Y5hXnc+cz6yivU1OTSKI5nhXipgDXmtlngGuAi7yJFCyl2/czr3gIZuZ3lKhLSjJ+ft1sUpONLz6ygqaWdr8jiUgU9fUqpt8DdwFnAPPCt7ifxbWmoYXK+kPMHZdY/Q89jRqcyS+un82W2mZu+t9lHDjONSNqGlp4fWMtT6+s5MW1Nezad9CjpCISaceai6lbCTDNJVhvZWl3/0OCdVAf7swTC/j5dbP58qMruOred7n3xjlMLDjyMiCbdzfxwtoaXlhbw/rwMq09zR2Xz7cumcIp4xOrX0ck1vS1QKwFRgLVHmYJnNLt+8lMTWZqYfwsMdpfl80sJDfjFL7y2EouvWcRnywpYuGMQsYNzaKrC7btPcDSbXt5cW0NW+sOAKFC8N2FU5g9Np+h2Wk0t3awuHwvv313B/9w33t8+bwT+OoFk0hKSrzmO5FY0NcCMQwoM7OlQGv3RufcFZ6kCojSHfuYNWYwqcnH01UTv86aVMCLd5zF3S9v4vHlFTyy5MMLDCUnGadOGMJnTy/m4ukjGTEo4yP7mFk0mE+fWswPnlnLf/1tC/UH2/nRldMTso9HJOj6WiDu9DJEEDUcbGddVSN3nD/J7yiBMmJQBv9+zUy+97GprNpVT8X+Q6QkGaMHZ3JSUR6DMo49wV9mWjL/fvVM8rPSuO+tckbnZ/KFsydGIb2IHI8+FQjn3JtmNg440Tn3qpllAXE978R75XtxDk4/YajfUQIpNyOVM08s6Pf7zYxvXzqFiv2H+M+XNjJnbL76JEQCpq9XMf0j8ARwX3jTaOBpr0IFwbtb95CVlhyXS4wGhZnx06tnMCY/k68/vopDbZ1+RxKRHvrauH47sABoBHDObQaGexUqCN7dupd5xUNIS1H/g5dyM1L56dUz2bXvEP/9+ma/44hID3397dfqnGvrfmBmKUDcXvK6u7GFLbXNLFDzUlScOmEoV88p4v63ytm+54DfcUQkrK8F4k0z+y6QaWYXAo8Df/Uulr/e3boHgNMnDvM5SeL41qWTSU4yfvbqJr+jiEhYXwvEt4E6YA3wf4Dnge95FcpvizbvYXBWKtM0/iFqhudmcNOC8Tyzuoqyqo8OrhOR6OvrZH1dhDqlv+icu8Y590C8jqru6nK8ubGOsycVaABXlH3hrInkpKfwy9e3+B1FRDhGgbCQO81sD7AR2BheTe770YkXfasr6tl7oI3zpsR1H3wg5WWlcsP8cbywtpqdezVnk4jfjnUG8VVCVy/Nc84Ncc4NAeYDC8zsq56n88HrG2pJMjh7Uv+v8Zf+u2lBMclJxm/eLvc7ikjCO1aB+DRwvXNuW/cG51w5cCPwGS+D+eW1DbXMHZfP4Kw0v6MkpBGDMvjE7NH8qXQX+w60HfsNIuKZYxWIVOfcnsM3OufqgKPOqWBmY8zsdTMrM7N1ZvaVgQSNht2NLayrauRcNS/56pYzJ9DS3sXjpbv8jiKS0I5VII72J9yx/rzrAL7unJsGnArcbmbTjidctL1SthuA86eM8DlJYps0IpdTxg/hD0t30tUVl9dCiMSEYxWIk82ssZdbEzDjaG90zlU751aE7zcB6wlN0RFYz71fzcSCbCaNOPJaBxIdN8wfy469B3l7y0dOYEUkSo5aIJxzyc65Qb3ccp1zx562M8zMioHZwJJenrvVzErNrLSuru5480dMbVMLS7bt5WMzR2nq6QC45KSRDM1O4+HFO/yOIpKwPJ9oyMxygCeBO5xzHxkB5Zy73zlX4pwrKSjw78qhF9fW0OVCC+OI/9JTkvlkyRhe21BLdcMhv+OIJCRPC4SZpRIqDo84557y8lgD9ez71UwakcOkEbl+R5GwT50yls4ux1MrKv2OIpKQPCsQFmqn+R9gvXPubq+OEwlV9YdYtn0fl80Y5XcU6WHs0Czmjx/CE8sriNOB+yKB5uUZxAJC4yjOM7NV4dtCD4/Xb4+XVgBw1ZxA96EnpGvmFrFtzwFW7NzvdxSRhONZgXDOve2cM+fcTOfcrPDtea+O119dXY7Hl+9iwcRhjBmS5XccOczCGYVkpSXzxPIKv6OIJJyEXw3n3a17qdh/iH+YN8bvKNKL7PQULj2pkGdXV2vFOZEoS/gC8diyneRlpnLRNA2OC6pr5hbR1NrBy2U1fkcRSSgJXSBqGlp4cW0NV88pIiM12e84cgTzxw+hKD9TzUwiUZbQBeK3722nyzluWlDsdxQ5iqQk4+o5Rby9ZY/GRIhEUcIWiAOtHTyyeAcXTx+pzukYcPWcIpxDYyJEoihhC8QTyytobOngljPH+x1F+mDs0CxOGT+EJzUmQiRqErJAtLR38qs3tlIyLp85Y/P9jiN9dM3cIsr3HGDlrnq/o4gkhIQsEI8t3UlNYwtfu3CSJuaLIQtnFJKZqjERItGScAWipb2TX76xlfnjh3DaxKF+x5HjkJOewqUnjeSvq6toadeYCBGvJVyB+P17O6hratXZQ4y6Zm4RTS0dHyzuJCLeSagCUdfUyi9e28xZkwqYP0FnD7Ho1AlDGT1YYyJEoiGhCsRPnl9Pa0cXd14e6JVP5SiSkoyr5oxm0eY6dje2+B1HJK4lTIFYUr6Xp1ZWcutZE5hQoCVFY9nVc4rocvDnlRoTIeKlhCgQ7Z1dfP8v6xg9OJPbzz3B7zgyQMXDsikZl691IkQ8lhAF4nfv7WDj7ia+f/k0MtM051I8uGZuEVtqm1ld0eB3FJG4FfcForaxhZ+9solzJhdoxtY4snBmIRmpSTypzmoRz8R9gfi359fT1tHFnZdP12WtcWRQRioXTx/JM6uraO3QmAgRL8R1gVhcvpenV1XxhbMnUDws2+84EmHXzC2i4VA7L6/TmAgRL8RtgQh1TK+lKD+T285Rx3Q8Ci0Tm8nDi3f4HUUkLsVtgXjone1s2t3MDy6fro7pOJWUZNwwfxxLtu1j8+4mv+OIxJ24LBA1DS3c8+omzp8ynAvVMR3XPjm3iLTkJB5ZstPvKCJxJy4LxL8+v572LscPLp/udxTx2NCcdBbOGMmTyys40NrhdxyRuBJ3BWJx+V7+urqK286eyNihWikuEXz6tHE0tXbwzOoqv6OIxJW4KhAdnV3c+UxoxPRt50z0O45EyZyx+UwZmctv392ukdUiERRXBeLRpTvZUNPE9y6bSkaqOqYThZlx8xnj2VDTxFub9/gdRyRuxE2B2H+gjbte3sTpE4dyyUkj/Y4jUXblrNGMGJTOfW9u9TuKSNyImwLx/17ZSHNrBz/QiOmElJaSxOcXjOfdrXtZW6n5mUQiIS4KxObdTfxhyU4+feo4Jo/M9TuO+OT6+WPJSU/hvrfK/Y4iEhfiokDc/comstJS+KfzT/Q7ivhoUEYqN8wfy3PvV7Fj7wG/44jEvJgvEGsqGnhhbQ03nzGeIdlpfscRn918xnhSk5P4+Wub/Y4iEvM8KxBm9qCZ1ZrZWq+OAXDXyxsZnJXKLWeO9/IwEiOGD8rgs6cX8/TKSrbUavoNkYHw8gziIeASD/fP0m37eHNTHbedPZHcjFQvDyUx5AtnTyQzNZmfvaKzCJGB8KxAOOfeAvZ5uH/uemkjBbnpfOa0Yq8OIzFoSHYaN58xnufWVLNGK86J9JvvfRBmdquZlZpZaV1dXZ/f99bmPSzdvo9/Ou8EzdYqH3HLWRMYmp3GD/+6TqOrRfrJ9wLhnLvfOVfinCspKCjo63u466WNFOVncu28sR4nlFg0KCOVf754MqU79muOJpF+8r1A9MdL62pYU9nAHRdMIi0lJv8TJAo+WTKGGaPz+MnzGzjYppleRY5XzP127exy3PXyJiYWZPOJ2aP9jiMBlpxk3HnFNGoaW7j75U1+xxGJOV5e5voo8B4w2cwqzOzmSOz3L6sq2VLbzNcvmkxykqbUkKObO24IN8wfy4PvbGPFzv1+xxGJKV5exXS9c67QOZfqnCtyzv3PQPfZ1tHFz17dxPRRg7hkuibkk7759qVTGDkog28+8T4t7Z1+xxGJGTHVxPSn0l3s2neIb1w0mSSdPUgf5Wak8m9XzWBLbTN3vbTR7zgiMSNmCkRLeyf/9bfNlIzL55zJfbvaSaTbOZOHc+OpY/nN29v424bdfscRiQkxUyB+/94Odje28o2LJ2s6b+mX7102jSkjc/n6n1ZT09DidxyRwIuJAtHU0s69b2zhzBOHceqEoX7HkRiVkZrML2+YQ2tHF//06EraOrr8jiQSaDFRIB58ezv7D7bzjYsm+x1FYtzEghx+ctUMlm7fxw+eWatR1iJHkeJ3gGPZf6CNBxaVc/H0EZw8ZrDfcSQOXDlrNBtrmrj3ja2cODyXz5+hmYBFehP4M4hfvbmVA20dfO1CnT1I5HzjoslcNG0EP36ujNc31vodRySQAl0gquoP8dC72/nE7NFaSlQiKinJ+Nm1s5g8chC3P7KClRpEJ/IRgS4Q97y6CRx87cJJfkeROJSdnsJvb5rHsJx0bnpoGZt3a4EhkZ4CWyA2727iieUV3HjqOIrys/yOI3Fq+KAMHr55PqnJSXz6f5ZSsf+g35FEAiOwBeI/X9pIVloKXzrvBL+jSJwbOzSL333+FA62dXDDb5ZQVX/I70gigRDIArFi535eLtvNrWdNYEh2mt9xJAFMLRzEQ58/hX3NbVx7/3s6kxAhgAWiq8vx42fLGJaTzs26/FCiaM7YfB6+ZT4NB9u59r7F7NqnIiGJLXAF4ulVlazYWc+3LplMdnrgh2lInDl5zGAeueVUmls7uPa+9yiva/Y7kohvAlUgupzjJy9s4OQxg7l6TpHfcSRBzSjK4w//OJ/Wji6u/tW7WkdCElagCkRtYyt1Ta388Irpms5bfDV9VB5P3nY6gzJT+dQDi3m1TDPASuIJVIHY09zKJ+cWMUtTakgAFA/L5snbTmfSiFxu/X0pv1+8w+9IIlFlQZqsLHv0JFexaS35unJJAuRAawdffnQlf9tQy3XzxnDnFdPJSE32O5YIAGa23DlX4sW+A3UGMWpwpoqDBE52egoPfKaEL517Ao8t28W19y/WWAlJCIEqEHmZqX5HEOlVcpLxjYsn8+sb57K1tpmFv1jEc+9X+x1LxFOBKhAiQXfJSSP5y5cWMHZIFrf/YQV3PLaShoPtfscS8YQKhMhxmliQw5O3nc4dF5zIX9+v5vy73+BPy3bR1RWc/jyRSFCBEOmH1OQk7rhgEn+5fQHjhmbzzSff5xP3vsN7W/f6HU0kYlQgRAbgpNF5PPGF0/jZtSdT09jC9Q8s5h9+/R6LNtdpOVOJeYG6zLWkpMSVlpb6HUOkX1raO/njsl386o2t1AWSy1oAAAiuSURBVDS2cMLwHG6YP5arZheRl6ULMMQbXl7mqgIhEmGtHZ08s6qKR5bsZNWuetKSkzhr0jAWzijkgmkjGJShYiGRowIhEqPWVjbw55WVvLCmmqqGFtKSk5g3Pp8zTyzgjBOGMa1wkKaVkQFRgRCJcV1djlUV9by4toa3NtWxoSa0vOnQ7DROP2EY84rzKRk3hMkjc0lWwZDjoAIhEmdqG1t4e8seFm3ew7tb97C7sRWA3PQUZo/LZ964fOYW5zNrzGCy0jTtvRyZCoRIHHPOUbH/EMt37GfZ9n2Ubt/Pxt2hM4wkgxOH5zKjKI8Zo/OYUZTHtMJBmgtKPhCzBcLMLgF+DiQDv3HO/fRor1eBEAlpONjOip37WbFzP2sqG1hT0cDeA21AaNqPE4fnMGVkLhMLcpg4PIeJBTkUD8siPUWFI9HEZIEws2RgE3AhUAEsA653zpUd6T0qECK9c85R3dDCmsoG1lY28H5FA1tqm6nsMWlgksHw3AxG5mVQmJfBiEGh+/lZqeRlpjIoI5VBmaH7GanJpKcmkZacRHpKEmbq94hVXhYILxs3TwG2OOfKAczsMeBK4IgFQkR6Z2aMGpzJqMGZXDx95AfbD7Z1UF53gK11zWytO0B1/SFqGlvYXNvMW5vqONDW2af9dxeK1JQkkix0PAOSzP7+2MCse1voeQwiWVoiVag+e3oxnz51XET2lci8LBCjgV09HlcA8w9/kZndCtwKMHbsWA/jiMSfrLQUThqdx0mj83p9vrm1g/qDbTQe6qDhUDsNh9ppbGmnpb2Tto4uWsO30P1O2ju76HLgXOisxbnQUsBdDhzug+1d4e0RbX+I4M6GatmAiPD98gjn3P3A/RBqYvI5jkhcyUlPISc9BfL9TiKxyMu5mCqBMT0eF4W3iYhIDPCyQCwDTjSz8WaWBlwHPOPh8UREJII8a2JyznWY2ZeAlwhd5vqgc26dV8cTEZHI8rQPwjn3PPC8l8cQERFvaD0IERHplQqEiIj0SgVCRER6pQIhIiK9CtRsrmbWBGz0O8cxDAP2+B2iD5QzspQzspQzciY753K92LHvI6kPs9GrSacixcxKg54RlDPSlDOylDNyzMyzGU7VxCQiIr1SgRARkV4FrUDc73eAPoiFjKCckaackaWckeNZxkB1UouISHAE7QxCREQCQgVCRER6FYgCYWaXmNlGM9tiZt+O0jEfNLNaM1vbY9sQM3vFzDaHf+aHt5uZ/SKc730zm9PjPZ8Nv36zmX22x/a5ZrYm/J5fWD/WUjSzMWb2upmVmdk6M/tKQHNmmNlSM1sdzvnD8PbxZrYkvO8/hqd9x8zSw4+3hJ8v7rGv74S3bzSzi3tsj9h3xMySzWylmT0b1Jxmtj38uazqvowxaJ97eD+DzewJM9tgZuvN7LSg5TSzyeF/x+5bo5ndEcCcXw3//7PWzB610P9X/n43Q8sK+ncjNBX4VmACkAasBqZF4bhnAXOAtT22/Qfw7fD9bwP/Hr6/EHiB0PK7pwJLwtuHAOXhn/nh+/nh55aGX2vh917aj4yFwJzw/VxgEzAtgDkNyAnfTwWWhPf5J+C68PZfA7eF738R+HX4/nXAH8P3p4U//3RgfPh7kRzp7wjwNeAPwLPhx4HLCWwHhh22LVCfe3g/vwVuCd9PAwYHMWePvMlADTAuSDkJLdG8Dcjs8Z38nN/fTU9/CffxH+Y04KUej78DfCdKxy7mwwViI1AYvl9IaOAewH3A9Ye/DrgeuK/H9vvC2wqBDT22f+h1A8j7F+DCIOcEsoAVhNYf3wOkHP45E1oj5LTw/ZTw6+zwz777dZH8jhBa2fA14Dzg2fBxg5hzOx8tEIH63IE8Qr/ULMg5D8t2EfBO0HISKhC7CBWflPB382K/v5tBaGLq/ofpVhHe5ocRzrnq8P0aYET4/pEyHm17RS/b+y18Cjmb0F/ngctpoWabVUAt8Aqhv1bqnXMdvez7gzzh5xuAof3I3x/3AN8EusKPhwY0pwNeNrPlZnZreFvQPvfxQB3wvxZqsvuNmWUHMGdP1wGPhu8HJqdzrhK4C9gJVBP6ri3H5+9mEApEILlQmQ3ENcBmlgM8CdzhnGvs+VxQcjrnOp1zswj9hX4KMMXnSB9hZh8Dap1zy/3O0gdnOOfmAJcCt5vZWT2fDMjnnkKomfZXzrnZwAFCTTUfCEhOAMLt91cAjx/+nN85w/0fVxIquqOAbOASv/J0C0KBqATG9HhcFN7mh91mVggQ/lkb3n6kjEfbXtTL9uNmZqmEisMjzrmngpqzm3OuHnid0CntYDPrnu+r574/yBN+Pg/Y24/8x2sBcIWZbQceI9TM9PMA5uz+ixLnXC3wZ0JFN2ifewVQ4ZxbEn78BKGCEbSc3S4FVjjndocfBynnBcA251ydc64deIrQ99Xf7+ZA2vMicSP0V0g5ocrZ3XkyPUrHLubDfRD/yYc7rf4jfP8yPtxptTS8fQihNtj88G0bMCT83OGdVgv7kc+A3wH3HLY9aDkLgMHh+5nAIuBjhP5S69nB9sXw/dv5cAfbn8L3p/PhDrZyQp1rEf+OAOfw907qQOUk9Ndjbo/77xL6azJQn3t4P4sIzSYKcGc4Y+Byhvf1GHBTEP8/ItRnt45QH54R6vz/st/fTc9/CffxH2choSt0tgL/EqVjPkqora+d0F9CNxNqw3sN2Ay82uPDN+CX4XxrgJIe+/k8sCV86/nlKwHWht/z3xzWkdfHjGcQOu19H1gVvi0MYM6ZwMpwzrXA98PbJ4T/x9kS/qKnh7dnhB9vCT8/oce+/iWcZSM9rgSJ9HeEDxeIQOUM51kdvq3r3k/QPvfwfmYBpeHP/mlCvziDmDOb0F/YeT22BSon8ENgQ3g/vyf0S97X76am2hARkV4FoQ9CREQCSAVCRER6pQIhIiK9UoEQEZFeqUCIiEivVCBERKRXKhAiItKr/w/XaVG055z2NAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 34.删除最后一列categories"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>createTime</th>\n",
       "      <th>education</th>\n",
       "      <th>salary</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>03-16</td>\n",
       "      <td>本科</td>\n",
       "      <td>27500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>03-16</td>\n",
       "      <td>本科</td>\n",
       "      <td>30000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>03-16</td>\n",
       "      <td>不限</td>\n",
       "      <td>27500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>03-16</td>\n",
       "      <td>本科</td>\n",
       "      <td>16500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>03-16</td>\n",
       "      <td>本科</td>\n",
       "      <td>15000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>130</th>\n",
       "      <td>03-16</td>\n",
       "      <td>本科</td>\n",
       "      <td>14000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>131</th>\n",
       "      <td>03-16</td>\n",
       "      <td>硕士</td>\n",
       "      <td>37500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>132</th>\n",
       "      <td>03-16</td>\n",
       "      <td>本科</td>\n",
       "      <td>30000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>133</th>\n",
       "      <td>03-16</td>\n",
       "      <td>本科</td>\n",
       "      <td>19000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>134</th>\n",
       "      <td>03-16</td>\n",
       "      <td>本科</td>\n",
       "      <td>30000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>135 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    createTime education salary\n",
       "0        03-16        本科  27500\n",
       "1        03-16        本科  30000\n",
       "2        03-16        不限  27500\n",
       "3        03-16        本科  16500\n",
       "4        03-16        本科  15000\n",
       "..         ...       ...    ...\n",
       "130      03-16        本科  14000\n",
       "131      03-16        硕士  37500\n",
       "132      03-16        本科  30000\n",
       "133      03-16        本科  19000\n",
       "134      03-16        本科  30000\n",
       "\n",
       "[135 rows x 3 columns]"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 35.将df的第一列与第二列合并为新的一列"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>createTime</th>\n",
       "      <th>education</th>\n",
       "      <th>salary</th>\n",
       "      <th>test</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>03-16</td>\n",
       "      <td>本科</td>\n",
       "      <td>27500</td>\n",
       "      <td>本科03-16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>03-16</td>\n",
       "      <td>本科</td>\n",
       "      <td>30000</td>\n",
       "      <td>本科03-16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>03-16</td>\n",
       "      <td>不限</td>\n",
       "      <td>27500</td>\n",
       "      <td>不限03-16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>03-16</td>\n",
       "      <td>本科</td>\n",
       "      <td>16500</td>\n",
       "      <td>本科03-16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>03-16</td>\n",
       "      <td>本科</td>\n",
       "      <td>15000</td>\n",
       "      <td>本科03-16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>130</th>\n",
       "      <td>03-16</td>\n",
       "      <td>本科</td>\n",
       "      <td>14000</td>\n",
       "      <td>本科03-16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>131</th>\n",
       "      <td>03-16</td>\n",
       "      <td>硕士</td>\n",
       "      <td>37500</td>\n",
       "      <td>硕士03-16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>132</th>\n",
       "      <td>03-16</td>\n",
       "      <td>本科</td>\n",
       "      <td>30000</td>\n",
       "      <td>本科03-16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>133</th>\n",
       "      <td>03-16</td>\n",
       "      <td>本科</td>\n",
       "      <td>19000</td>\n",
       "      <td>本科03-16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>134</th>\n",
       "      <td>03-16</td>\n",
       "      <td>本科</td>\n",
       "      <td>30000</td>\n",
       "      <td>本科03-16</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>135 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    createTime education salary     test\n",
       "0        03-16        本科  27500  本科03-16\n",
       "1        03-16        本科  30000  本科03-16\n",
       "2        03-16        不限  27500  不限03-16\n",
       "3        03-16        本科  16500  本科03-16\n",
       "4        03-16        本科  15000  本科03-16\n",
       "..         ...       ...    ...      ...\n",
       "130      03-16        本科  14000  本科03-16\n",
       "131      03-16        硕士  37500  硕士03-16\n",
       "132      03-16        本科  30000  本科03-16\n",
       "133      03-16        本科  19000  本科03-16\n",
       "134      03-16        本科  30000  本科03-16\n",
       "\n",
       "[135 rows x 4 columns]"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 36.将education列与salary列合并为新的一列"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>createTime</th>\n",
       "      <th>education</th>\n",
       "      <th>salary</th>\n",
       "      <th>test</th>\n",
       "      <th>test1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>03-16</td>\n",
       "      <td>本科</td>\n",
       "      <td>27500</td>\n",
       "      <td>本科03-16</td>\n",
       "      <td>27500.0本科</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>03-16</td>\n",
       "      <td>本科</td>\n",
       "      <td>30000</td>\n",
       "      <td>本科03-16</td>\n",
       "      <td>30000.0本科</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>03-16</td>\n",
       "      <td>不限</td>\n",
       "      <td>27500</td>\n",
       "      <td>不限03-16</td>\n",
       "      <td>27500.0不限</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>03-16</td>\n",
       "      <td>本科</td>\n",
       "      <td>16500</td>\n",
       "      <td>本科03-16</td>\n",
       "      <td>16500.0本科</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>03-16</td>\n",
       "      <td>本科</td>\n",
       "      <td>15000</td>\n",
       "      <td>本科03-16</td>\n",
       "      <td>15000.0本科</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>130</th>\n",
       "      <td>03-16</td>\n",
       "      <td>本科</td>\n",
       "      <td>14000</td>\n",
       "      <td>本科03-16</td>\n",
       "      <td>14000.0本科</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>131</th>\n",
       "      <td>03-16</td>\n",
       "      <td>硕士</td>\n",
       "      <td>37500</td>\n",
       "      <td>硕士03-16</td>\n",
       "      <td>37500.0硕士</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>132</th>\n",
       "      <td>03-16</td>\n",
       "      <td>本科</td>\n",
       "      <td>30000</td>\n",
       "      <td>本科03-16</td>\n",
       "      <td>30000.0本科</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>133</th>\n",
       "      <td>03-16</td>\n",
       "      <td>本科</td>\n",
       "      <td>19000</td>\n",
       "      <td>本科03-16</td>\n",
       "      <td>19000.0本科</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>134</th>\n",
       "      <td>03-16</td>\n",
       "      <td>本科</td>\n",
       "      <td>30000</td>\n",
       "      <td>本科03-16</td>\n",
       "      <td>30000.0本科</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>135 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    createTime education salary     test      test1\n",
       "0        03-16        本科  27500  本科03-16  27500.0本科\n",
       "1        03-16        本科  30000  本科03-16  30000.0本科\n",
       "2        03-16        不限  27500  不限03-16  27500.0不限\n",
       "3        03-16        本科  16500  本科03-16  16500.0本科\n",
       "4        03-16        本科  15000  本科03-16  15000.0本科\n",
       "..         ...       ...    ...      ...        ...\n",
       "130      03-16        本科  14000  本科03-16  14000.0本科\n",
       "131      03-16        硕士  37500  硕士03-16  37500.0硕士\n",
       "132      03-16        本科  30000  本科03-16  30000.0本科\n",
       "133      03-16        本科  19000  本科03-16  19000.0本科\n",
       "134      03-16        本科  30000  本科03-16  30000.0本科\n",
       "\n",
       "[135 rows x 5 columns]"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 37.计算salary最大值与最小值之差"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "salary    41500.0\n",
       "dtype: float64"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 38.将第一行与最后一行拼接"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>createTime</th>\n",
       "      <th>education</th>\n",
       "      <th>salary</th>\n",
       "      <th>test</th>\n",
       "      <th>test1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>03-16</td>\n",
       "      <td>本科</td>\n",
       "      <td>27500</td>\n",
       "      <td>本科03-16</td>\n",
       "      <td>27500.0本科</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>133</th>\n",
       "      <td>03-16</td>\n",
       "      <td>本科</td>\n",
       "      <td>19000</td>\n",
       "      <td>本科03-16</td>\n",
       "      <td>19000.0本科</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    createTime education salary     test      test1\n",
       "0        03-16        本科  27500  本科03-16  27500.0本科\n",
       "133      03-16        本科  19000  本科03-16  19000.0本科"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 39.将第8行数据添加至末尾"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>createTime</th>\n",
       "      <th>education</th>\n",
       "      <th>salary</th>\n",
       "      <th>test</th>\n",
       "      <th>test1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>03-16</td>\n",
       "      <td>本科</td>\n",
       "      <td>27500</td>\n",
       "      <td>本科03-16</td>\n",
       "      <td>27500.0本科</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>03-16</td>\n",
       "      <td>本科</td>\n",
       "      <td>30000</td>\n",
       "      <td>本科03-16</td>\n",
       "      <td>30000.0本科</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>03-16</td>\n",
       "      <td>不限</td>\n",
       "      <td>27500</td>\n",
       "      <td>不限03-16</td>\n",
       "      <td>27500.0不限</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>03-16</td>\n",
       "      <td>本科</td>\n",
       "      <td>16500</td>\n",
       "      <td>本科03-16</td>\n",
       "      <td>16500.0本科</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>03-16</td>\n",
       "      <td>本科</td>\n",
       "      <td>15000</td>\n",
       "      <td>本科03-16</td>\n",
       "      <td>15000.0本科</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>131</th>\n",
       "      <td>03-16</td>\n",
       "      <td>硕士</td>\n",
       "      <td>37500</td>\n",
       "      <td>硕士03-16</td>\n",
       "      <td>37500.0硕士</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>132</th>\n",
       "      <td>03-16</td>\n",
       "      <td>本科</td>\n",
       "      <td>30000</td>\n",
       "      <td>本科03-16</td>\n",
       "      <td>30000.0本科</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>133</th>\n",
       "      <td>03-16</td>\n",
       "      <td>本科</td>\n",
       "      <td>19000</td>\n",
       "      <td>本科03-16</td>\n",
       "      <td>19000.0本科</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>134</th>\n",
       "      <td>03-16</td>\n",
       "      <td>本科</td>\n",
       "      <td>30000</td>\n",
       "      <td>本科03-16</td>\n",
       "      <td>30000.0本科</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>03-16</td>\n",
       "      <td>本科</td>\n",
       "      <td>12500</td>\n",
       "      <td>本科03-16</td>\n",
       "      <td>12500.0本科</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>136 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    createTime education salary     test      test1\n",
       "0        03-16        本科  27500  本科03-16  27500.0本科\n",
       "1        03-16        本科  30000  本科03-16  30000.0本科\n",
       "2        03-16        不限  27500  不限03-16  27500.0不限\n",
       "3        03-16        本科  16500  本科03-16  16500.0本科\n",
       "4        03-16        本科  15000  本科03-16  15000.0本科\n",
       "..         ...       ...    ...      ...        ...\n",
       "131      03-16        硕士  37500  硕士03-16  37500.0硕士\n",
       "132      03-16        本科  30000  本科03-16  30000.0本科\n",
       "133      03-16        本科  19000  本科03-16  19000.0本科\n",
       "134      03-16        本科  30000  本科03-16  30000.0本科\n",
       "7        03-16        本科  12500  本科03-16  12500.0本科\n",
       "\n",
       "[136 rows x 5 columns]"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 40.查看每列的数据类型"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "createTime    object\n",
       "education     object\n",
       "salary        object\n",
       "test          object\n",
       "test1         object\n",
       "dtype: object"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 41.将createTime列设置为索引"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>education</th>\n",
       "      <th>salary</th>\n",
       "      <th>test</th>\n",
       "      <th>test1</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>createTime</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>03-16</th>\n",
       "      <td>本科</td>\n",
       "      <td>27500</td>\n",
       "      <td>本科03-16</td>\n",
       "      <td>27500.0本科</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>03-16</th>\n",
       "      <td>本科</td>\n",
       "      <td>30000</td>\n",
       "      <td>本科03-16</td>\n",
       "      <td>30000.0本科</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>03-16</th>\n",
       "      <td>不限</td>\n",
       "      <td>27500</td>\n",
       "      <td>不限03-16</td>\n",
       "      <td>27500.0不限</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>03-16</th>\n",
       "      <td>本科</td>\n",
       "      <td>16500</td>\n",
       "      <td>本科03-16</td>\n",
       "      <td>16500.0本科</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>03-16</th>\n",
       "      <td>本科</td>\n",
       "      <td>15000</td>\n",
       "      <td>本科03-16</td>\n",
       "      <td>15000.0本科</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>03-16</th>\n",
       "      <td>本科</td>\n",
       "      <td>14000</td>\n",
       "      <td>本科03-16</td>\n",
       "      <td>14000.0本科</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>03-16</th>\n",
       "      <td>硕士</td>\n",
       "      <td>37500</td>\n",
       "      <td>硕士03-16</td>\n",
       "      <td>37500.0硕士</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>03-16</th>\n",
       "      <td>本科</td>\n",
       "      <td>30000</td>\n",
       "      <td>本科03-16</td>\n",
       "      <td>30000.0本科</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>03-16</th>\n",
       "      <td>本科</td>\n",
       "      <td>19000</td>\n",
       "      <td>本科03-16</td>\n",
       "      <td>19000.0本科</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>03-16</th>\n",
       "      <td>本科</td>\n",
       "      <td>30000</td>\n",
       "      <td>本科03-16</td>\n",
       "      <td>30000.0本科</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>135 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           education salary     test      test1\n",
       "createTime                                     \n",
       "03-16             本科  27500  本科03-16  27500.0本科\n",
       "03-16             本科  30000  本科03-16  30000.0本科\n",
       "03-16             不限  27500  不限03-16  27500.0不限\n",
       "03-16             本科  16500  本科03-16  16500.0本科\n",
       "03-16             本科  15000  本科03-16  15000.0本科\n",
       "...              ...    ...      ...        ...\n",
       "03-16             本科  14000  本科03-16  14000.0本科\n",
       "03-16             硕士  37500  硕士03-16  37500.0硕士\n",
       "03-16             本科  30000  本科03-16  30000.0本科\n",
       "03-16             本科  19000  本科03-16  19000.0本科\n",
       "03-16             本科  30000  本科03-16  30000.0本科\n",
       "\n",
       "[135 rows x 4 columns]"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 42.生成一个和df长度相同的随机数dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>130</th>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>131</th>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>132</th>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>133</th>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>134</th>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>135 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     0\n",
       "0    6\n",
       "1    2\n",
       "2    6\n",
       "3    8\n",
       "4    7\n",
       "..  ..\n",
       "130  6\n",
       "131  8\n",
       "132  2\n",
       "133  8\n",
       "134  3\n",
       "\n",
       "[135 rows x 1 columns]"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 43.将上一题生成的dataframe与df合并"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>createTime</th>\n",
       "      <th>education</th>\n",
       "      <th>salary</th>\n",
       "      <th>test</th>\n",
       "      <th>test1</th>\n",
       "      <th>0</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>03-16</td>\n",
       "      <td>本科</td>\n",
       "      <td>27500</td>\n",
       "      <td>本科03-16</td>\n",
       "      <td>27500.0本科</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>03-16</td>\n",
       "      <td>本科</td>\n",
       "      <td>30000</td>\n",
       "      <td>本科03-16</td>\n",
       "      <td>30000.0本科</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>03-16</td>\n",
       "      <td>不限</td>\n",
       "      <td>27500</td>\n",
       "      <td>不限03-16</td>\n",
       "      <td>27500.0不限</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>03-16</td>\n",
       "      <td>本科</td>\n",
       "      <td>16500</td>\n",
       "      <td>本科03-16</td>\n",
       "      <td>16500.0本科</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>03-16</td>\n",
       "      <td>本科</td>\n",
       "      <td>15000</td>\n",
       "      <td>本科03-16</td>\n",
       "      <td>15000.0本科</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>130</th>\n",
       "      <td>03-16</td>\n",
       "      <td>本科</td>\n",
       "      <td>14000</td>\n",
       "      <td>本科03-16</td>\n",
       "      <td>14000.0本科</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>131</th>\n",
       "      <td>03-16</td>\n",
       "      <td>硕士</td>\n",
       "      <td>37500</td>\n",
       "      <td>硕士03-16</td>\n",
       "      <td>37500.0硕士</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>132</th>\n",
       "      <td>03-16</td>\n",
       "      <td>本科</td>\n",
       "      <td>30000</td>\n",
       "      <td>本科03-16</td>\n",
       "      <td>30000.0本科</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>133</th>\n",
       "      <td>03-16</td>\n",
       "      <td>本科</td>\n",
       "      <td>19000</td>\n",
       "      <td>本科03-16</td>\n",
       "      <td>19000.0本科</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>134</th>\n",
       "      <td>03-16</td>\n",
       "      <td>本科</td>\n",
       "      <td>30000</td>\n",
       "      <td>本科03-16</td>\n",
       "      <td>30000.0本科</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>135 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    createTime education salary     test      test1  0\n",
       "0        03-16        本科  27500  本科03-16  27500.0本科  6\n",
       "1        03-16        本科  30000  本科03-16  30000.0本科  2\n",
       "2        03-16        不限  27500  不限03-16  27500.0不限  6\n",
       "3        03-16        本科  16500  本科03-16  16500.0本科  8\n",
       "4        03-16        本科  15000  本科03-16  15000.0本科  7\n",
       "..         ...       ...    ...      ...        ... ..\n",
       "130      03-16        本科  14000  本科03-16  14000.0本科  6\n",
       "131      03-16        硕士  37500  硕士03-16  37500.0硕士  8\n",
       "132      03-16        本科  30000  本科03-16  30000.0本科  2\n",
       "133      03-16        本科  19000  本科03-16  19000.0本科  8\n",
       "134      03-16        本科  30000  本科03-16  30000.0本科  3\n",
       "\n",
       "[135 rows x 6 columns]"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 44.生成新的一列new为salary列减去之前生成随机数列"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>createTime</th>\n",
       "      <th>education</th>\n",
       "      <th>salary</th>\n",
       "      <th>test</th>\n",
       "      <th>test1</th>\n",
       "      <th>0</th>\n",
       "      <th>new</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>03-16</td>\n",
       "      <td>本科</td>\n",
       "      <td>27500</td>\n",
       "      <td>本科03-16</td>\n",
       "      <td>27500.0本科</td>\n",
       "      <td>6</td>\n",
       "      <td>27494</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>03-16</td>\n",
       "      <td>本科</td>\n",
       "      <td>30000</td>\n",
       "      <td>本科03-16</td>\n",
       "      <td>30000.0本科</td>\n",
       "      <td>2</td>\n",
       "      <td>29998</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>03-16</td>\n",
       "      <td>不限</td>\n",
       "      <td>27500</td>\n",
       "      <td>不限03-16</td>\n",
       "      <td>27500.0不限</td>\n",
       "      <td>6</td>\n",
       "      <td>27494</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>03-16</td>\n",
       "      <td>本科</td>\n",
       "      <td>16500</td>\n",
       "      <td>本科03-16</td>\n",
       "      <td>16500.0本科</td>\n",
       "      <td>8</td>\n",
       "      <td>16492</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>03-16</td>\n",
       "      <td>本科</td>\n",
       "      <td>15000</td>\n",
       "      <td>本科03-16</td>\n",
       "      <td>15000.0本科</td>\n",
       "      <td>7</td>\n",
       "      <td>14993</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>130</th>\n",
       "      <td>03-16</td>\n",
       "      <td>本科</td>\n",
       "      <td>14000</td>\n",
       "      <td>本科03-16</td>\n",
       "      <td>14000.0本科</td>\n",
       "      <td>6</td>\n",
       "      <td>13994</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>131</th>\n",
       "      <td>03-16</td>\n",
       "      <td>硕士</td>\n",
       "      <td>37500</td>\n",
       "      <td>硕士03-16</td>\n",
       "      <td>37500.0硕士</td>\n",
       "      <td>8</td>\n",
       "      <td>37492</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>132</th>\n",
       "      <td>03-16</td>\n",
       "      <td>本科</td>\n",
       "      <td>30000</td>\n",
       "      <td>本科03-16</td>\n",
       "      <td>30000.0本科</td>\n",
       "      <td>2</td>\n",
       "      <td>29998</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>133</th>\n",
       "      <td>03-16</td>\n",
       "      <td>本科</td>\n",
       "      <td>19000</td>\n",
       "      <td>本科03-16</td>\n",
       "      <td>19000.0本科</td>\n",
       "      <td>8</td>\n",
       "      <td>18992</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>134</th>\n",
       "      <td>03-16</td>\n",
       "      <td>本科</td>\n",
       "      <td>30000</td>\n",
       "      <td>本科03-16</td>\n",
       "      <td>30000.0本科</td>\n",
       "      <td>3</td>\n",
       "      <td>29997</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>135 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    createTime education salary     test      test1  0    new\n",
       "0        03-16        本科  27500  本科03-16  27500.0本科  6  27494\n",
       "1        03-16        本科  30000  本科03-16  30000.0本科  2  29998\n",
       "2        03-16        不限  27500  不限03-16  27500.0不限  6  27494\n",
       "3        03-16        本科  16500  本科03-16  16500.0本科  8  16492\n",
       "4        03-16        本科  15000  本科03-16  15000.0本科  7  14993\n",
       "..         ...       ...    ...      ...        ... ..    ...\n",
       "130      03-16        本科  14000  本科03-16  14000.0本科  6  13994\n",
       "131      03-16        硕士  37500  硕士03-16  37500.0硕士  8  37492\n",
       "132      03-16        本科  30000  本科03-16  30000.0本科  2  29998\n",
       "133      03-16        本科  19000  本科03-16  19000.0本科  8  18992\n",
       "134      03-16        本科  30000  本科03-16  30000.0本科  3  29997\n",
       "\n",
       "[135 rows x 7 columns]"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 45.检查数据中是否含有任何缺失值"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 46.将salary列类型转换为浮点数"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      27500.0\n",
       "1      30000.0\n",
       "2      27500.0\n",
       "3      16500.0\n",
       "4      15000.0\n",
       "        ...   \n",
       "130    14000.0\n",
       "131    37500.0\n",
       "132    30000.0\n",
       "133    19000.0\n",
       "134    30000.0\n",
       "Name: salary, Length: 135, dtype: float64"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 47.计算salary大于10000的次数"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "119"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 48.查看每种学历出现的次数"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "本科    119\n",
       "硕士      7\n",
       "不限      5\n",
       "大专      4\n",
       "Name: education, dtype: int64"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 49.查看education列共有几种学历"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 50.提取salary与new列的和大于60000的最后3行"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>createTime</th>\n",
       "      <th>education</th>\n",
       "      <th>salary</th>\n",
       "      <th>test</th>\n",
       "      <th>test1</th>\n",
       "      <th>0</th>\n",
       "      <th>new</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>92</th>\n",
       "      <td>03-16</td>\n",
       "      <td>本科</td>\n",
       "      <td>35000</td>\n",
       "      <td>本科03-16</td>\n",
       "      <td>35000.0本科</td>\n",
       "      <td>4</td>\n",
       "      <td>34996</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>101</th>\n",
       "      <td>03-16</td>\n",
       "      <td>本科</td>\n",
       "      <td>37500</td>\n",
       "      <td>本科03-16</td>\n",
       "      <td>37500.0本科</td>\n",
       "      <td>5</td>\n",
       "      <td>37495</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>131</th>\n",
       "      <td>03-16</td>\n",
       "      <td>硕士</td>\n",
       "      <td>37500</td>\n",
       "      <td>硕士03-16</td>\n",
       "      <td>37500.0硕士</td>\n",
       "      <td>8</td>\n",
       "      <td>37492</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    createTime education salary     test      test1  0    new\n",
       "92       03-16        本科  35000  本科03-16  35000.0本科  4  34996\n",
       "101      03-16        本科  37500  本科03-16  37500.0本科  5  37495\n",
       "131      03-16        硕士  37500  硕士03-16  37500.0硕士  8  37492"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 第三期 金融数据处理"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 51.使用绝对路径读取本地Excel数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING *** OLE2 inconsistency: SSCS size is 0 but SSAT size is non-zero\n"
     ]
    }
   ],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 52.查看数据前三行"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>代码</th>\n",
       "      <th>简称</th>\n",
       "      <th>日期</th>\n",
       "      <th>前收盘价(元)</th>\n",
       "      <th>开盘价(元)</th>\n",
       "      <th>最高价(元)</th>\n",
       "      <th>最低价(元)</th>\n",
       "      <th>收盘价(元)</th>\n",
       "      <th>成交量(股)</th>\n",
       "      <th>成交金额(元)</th>\n",
       "      <th>涨跌(元)</th>\n",
       "      <th>涨跌幅(%)</th>\n",
       "      <th>均价(元)</th>\n",
       "      <th>换手率(%)</th>\n",
       "      <th>A股流通市值(元)</th>\n",
       "      <th>总市值(元)</th>\n",
       "      <th>A股流通股本(股)</th>\n",
       "      <th>市盈率</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>600000.SH</td>\n",
       "      <td>浦发银行</td>\n",
       "      <td>2016-01-04</td>\n",
       "      <td>16.1356</td>\n",
       "      <td>16.1444</td>\n",
       "      <td>16.1444</td>\n",
       "      <td>15.4997</td>\n",
       "      <td>15.7205</td>\n",
       "      <td>42240610</td>\n",
       "      <td>754425783</td>\n",
       "      <td>-0.4151</td>\n",
       "      <td>-2.5725</td>\n",
       "      <td>17.8602</td>\n",
       "      <td>0.2264</td>\n",
       "      <td>3.320318e+11</td>\n",
       "      <td>3.320318e+11</td>\n",
       "      <td>1.865347e+10</td>\n",
       "      <td>6.5614</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>600000.SH</td>\n",
       "      <td>浦发银行</td>\n",
       "      <td>2016-01-05</td>\n",
       "      <td>15.7205</td>\n",
       "      <td>15.4644</td>\n",
       "      <td>15.9501</td>\n",
       "      <td>15.3672</td>\n",
       "      <td>15.8618</td>\n",
       "      <td>58054793</td>\n",
       "      <td>1034181474</td>\n",
       "      <td>0.1413</td>\n",
       "      <td>0.8989</td>\n",
       "      <td>17.8139</td>\n",
       "      <td>0.3112</td>\n",
       "      <td>3.350163e+11</td>\n",
       "      <td>3.350163e+11</td>\n",
       "      <td>1.865347e+10</td>\n",
       "      <td>6.6204</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>600000.SH</td>\n",
       "      <td>浦发银行</td>\n",
       "      <td>2016-01-06</td>\n",
       "      <td>15.8618</td>\n",
       "      <td>15.8088</td>\n",
       "      <td>16.0208</td>\n",
       "      <td>15.6234</td>\n",
       "      <td>15.9855</td>\n",
       "      <td>46772653</td>\n",
       "      <td>838667398</td>\n",
       "      <td>0.1236</td>\n",
       "      <td>0.7795</td>\n",
       "      <td>17.9307</td>\n",
       "      <td>0.2507</td>\n",
       "      <td>3.376278e+11</td>\n",
       "      <td>3.376278e+11</td>\n",
       "      <td>1.865347e+10</td>\n",
       "      <td>6.6720</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          代码    简称         日期  前收盘价(元)   开盘价(元)   最高价(元)   最低价(元)   收盘价(元)  \\\n",
       "0  600000.SH  浦发银行 2016-01-04  16.1356  16.1444  16.1444  15.4997  15.7205   \n",
       "1  600000.SH  浦发银行 2016-01-05  15.7205  15.4644  15.9501  15.3672  15.8618   \n",
       "2  600000.SH  浦发银行 2016-01-06  15.8618  15.8088  16.0208  15.6234  15.9855   \n",
       "\n",
       "     成交量(股)     成交金额(元)   涨跌(元)  涨跌幅(%)    均价(元)  换手率(%)     A股流通市值(元)  \\\n",
       "0  42240610   754425783 -0.4151 -2.5725  17.8602  0.2264  3.320318e+11   \n",
       "1  58054793  1034181474  0.1413  0.8989  17.8139  0.3112  3.350163e+11   \n",
       "2  46772653   838667398  0.1236  0.7795  17.9307  0.2507  3.376278e+11   \n",
       "\n",
       "         总市值(元)     A股流通股本(股)     市盈率  \n",
       "0  3.320318e+11  1.865347e+10  6.5614  \n",
       "1  3.350163e+11  1.865347e+10  6.6204  \n",
       "2  3.376278e+11  1.865347e+10  6.6720  "
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 53.查看每列数据缺失值情况"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "代码           1\n",
       "简称           2\n",
       "日期           2\n",
       "前收盘价(元)      2\n",
       "开盘价(元)       2\n",
       "最高价(元)       2\n",
       "最低价(元)       2\n",
       "收盘价(元)       2\n",
       "成交量(股)       2\n",
       "成交金额(元)      2\n",
       "涨跌(元)        2\n",
       "涨跌幅(%)       2\n",
       "均价(元)        2\n",
       "换手率(%)       2\n",
       "A股流通市值(元)    2\n",
       "总市值(元)       2\n",
       "A股流通股本(股)    2\n",
       "市盈率          2\n",
       "dtype: int64"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 54.提取日期列含有空值的行"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>代码</th>\n",
       "      <th>简称</th>\n",
       "      <th>日期</th>\n",
       "      <th>前收盘价(元)</th>\n",
       "      <th>开盘价(元)</th>\n",
       "      <th>最高价(元)</th>\n",
       "      <th>最低价(元)</th>\n",
       "      <th>收盘价(元)</th>\n",
       "      <th>成交量(股)</th>\n",
       "      <th>成交金额(元)</th>\n",
       "      <th>涨跌(元)</th>\n",
       "      <th>涨跌幅(%)</th>\n",
       "      <th>均价(元)</th>\n",
       "      <th>换手率(%)</th>\n",
       "      <th>A股流通市值(元)</th>\n",
       "      <th>总市值(元)</th>\n",
       "      <th>A股流通股本(股)</th>\n",
       "      <th>市盈率</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>327</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>328</th>\n",
       "      <td>数据来源：Wind资讯</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              代码   简称  日期  前收盘价(元)  开盘价(元)  最高价(元)  最低价(元)  收盘价(元) 成交量(股)  \\\n",
       "327          NaN  NaN NaT      NaN     NaN     NaN     NaN     NaN    NaN   \n",
       "328  数据来源：Wind资讯  NaN NaT      NaN     NaN     NaN     NaN     NaN    NaN   \n",
       "\n",
       "    成交金额(元)  涨跌(元)  涨跌幅(%) 均价(元) 换手率(%)  A股流通市值(元)  总市值(元)  A股流通股本(股)  市盈率  \n",
       "327     NaN    NaN     NaN   NaN    NaN        NaN     NaN        NaN  NaN  \n",
       "328     NaN    NaN     NaN   NaN    NaN        NaN     NaN        NaN  NaN  "
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 55.输出每列缺失值具体行数"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "列名：\"代码\", 第[327]行位置有缺失值\n",
      "列名：\"简称\", 第[327, 328]行位置有缺失值\n",
      "列名：\"日期\", 第[327, 328]行位置有缺失值\n",
      "列名：\"前收盘价(元)\", 第[327, 328]行位置有缺失值\n",
      "列名：\"开盘价(元)\", 第[327, 328]行位置有缺失值\n",
      "列名：\"最高价(元)\", 第[327, 328]行位置有缺失值\n",
      "列名：\"最低价(元)\", 第[327, 328]行位置有缺失值\n",
      "列名：\"收盘价(元)\", 第[327, 328]行位置有缺失值\n",
      "列名：\"成交量(股)\", 第[327, 328]行位置有缺失值\n",
      "列名：\"成交金额(元)\", 第[327, 328]行位置有缺失值\n",
      "列名：\"涨跌(元)\", 第[327, 328]行位置有缺失值\n",
      "列名：\"涨跌幅(%)\", 第[327, 328]行位置有缺失值\n",
      "列名：\"均价(元)\", 第[327, 328]行位置有缺失值\n",
      "列名：\"换手率(%)\", 第[327, 328]行位置有缺失值\n",
      "列名：\"A股流通市值(元)\", 第[327, 328]行位置有缺失值\n",
      "列名：\"总市值(元)\", 第[327, 328]行位置有缺失值\n",
      "列名：\"A股流通股本(股)\", 第[327, 328]行位置有缺失值\n",
      "列名：\"市盈率\", 第[327, 328]行位置有缺失值\n"
     ]
    }
   ],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 56.删除所有存在缺失值的行"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 57.绘制收盘价的折线图"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7ffb0886d4d0>"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 600x450 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 58.同时绘制开盘价与收盘价"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7ffb088e2150>"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:214: RuntimeWarning: Glyph 25910 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "/opt/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:214: RuntimeWarning: Glyph 30424 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "/opt/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:214: RuntimeWarning: Glyph 20215 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "/opt/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:214: RuntimeWarning: Glyph 20803 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "/opt/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:214: RuntimeWarning: Glyph 24320 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "/opt/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:183: RuntimeWarning: Glyph 25910 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "/opt/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:183: RuntimeWarning: Glyph 30424 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "/opt/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:183: RuntimeWarning: Glyph 20215 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "/opt/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:183: RuntimeWarning: Glyph 20803 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "/opt/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:183: RuntimeWarning: Glyph 24320 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 600x450 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 59.绘制涨跌幅的直方图"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7ffb089f13d0>"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 600x450 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 60.让直方图更细致"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7ffb08e01410>"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 600x450 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 61.以data的列名创建一个dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 62.打印所有换手率不是数字的行"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>代码</th>\n",
       "      <th>简称</th>\n",
       "      <th>日期</th>\n",
       "      <th>前收盘价(元)</th>\n",
       "      <th>开盘价(元)</th>\n",
       "      <th>最高价(元)</th>\n",
       "      <th>最低价(元)</th>\n",
       "      <th>收盘价(元)</th>\n",
       "      <th>成交量(股)</th>\n",
       "      <th>成交金额(元)</th>\n",
       "      <th>涨跌(元)</th>\n",
       "      <th>涨跌幅(%)</th>\n",
       "      <th>均价(元)</th>\n",
       "      <th>换手率(%)</th>\n",
       "      <th>A股流通市值(元)</th>\n",
       "      <th>总市值(元)</th>\n",
       "      <th>A股流通股本(股)</th>\n",
       "      <th>市盈率</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>600000.SH</td>\n",
       "      <td>浦发银行</td>\n",
       "      <td>2016-02-16</td>\n",
       "      <td>16.2946</td>\n",
       "      <td>16.2946</td>\n",
       "      <td>16.2946</td>\n",
       "      <td>16.2946</td>\n",
       "      <td>16.2946</td>\n",
       "      <td>--</td>\n",
       "      <td>--</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>--</td>\n",
       "      <td>--</td>\n",
       "      <td>3.441565e+11</td>\n",
       "      <td>3.441565e+11</td>\n",
       "      <td>1.865347e+10</td>\n",
       "      <td>6.801</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>600000.SH</td>\n",
       "      <td>浦发银行</td>\n",
       "      <td>2016-02-17</td>\n",
       "      <td>16.2946</td>\n",
       "      <td>16.2946</td>\n",
       "      <td>16.2946</td>\n",
       "      <td>16.2946</td>\n",
       "      <td>16.2946</td>\n",
       "      <td>--</td>\n",
       "      <td>--</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>--</td>\n",
       "      <td>--</td>\n",
       "      <td>3.441565e+11</td>\n",
       "      <td>3.441565e+11</td>\n",
       "      <td>1.865347e+10</td>\n",
       "      <td>6.801</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>600000.SH</td>\n",
       "      <td>浦发银行</td>\n",
       "      <td>2016-02-18</td>\n",
       "      <td>16.2946</td>\n",
       "      <td>16.2946</td>\n",
       "      <td>16.2946</td>\n",
       "      <td>16.2946</td>\n",
       "      <td>16.2946</td>\n",
       "      <td>--</td>\n",
       "      <td>--</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>--</td>\n",
       "      <td>--</td>\n",
       "      <td>3.441565e+11</td>\n",
       "      <td>3.441565e+11</td>\n",
       "      <td>1.865347e+10</td>\n",
       "      <td>6.801</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>600000.SH</td>\n",
       "      <td>浦发银行</td>\n",
       "      <td>2016-02-19</td>\n",
       "      <td>16.2946</td>\n",
       "      <td>16.2946</td>\n",
       "      <td>16.2946</td>\n",
       "      <td>16.2946</td>\n",
       "      <td>16.2946</td>\n",
       "      <td>--</td>\n",
       "      <td>--</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>--</td>\n",
       "      <td>--</td>\n",
       "      <td>3.441565e+11</td>\n",
       "      <td>3.441565e+11</td>\n",
       "      <td>1.865347e+10</td>\n",
       "      <td>6.801</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>600000.SH</td>\n",
       "      <td>浦发银行</td>\n",
       "      <td>2016-02-22</td>\n",
       "      <td>16.2946</td>\n",
       "      <td>16.2946</td>\n",
       "      <td>16.2946</td>\n",
       "      <td>16.2946</td>\n",
       "      <td>16.2946</td>\n",
       "      <td>--</td>\n",
       "      <td>--</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>--</td>\n",
       "      <td>--</td>\n",
       "      <td>3.441565e+11</td>\n",
       "      <td>3.441565e+11</td>\n",
       "      <td>1.865347e+10</td>\n",
       "      <td>6.801</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>600000.SH</td>\n",
       "      <td>浦发银行</td>\n",
       "      <td>2016-02-23</td>\n",
       "      <td>16.2946</td>\n",
       "      <td>16.2946</td>\n",
       "      <td>16.2946</td>\n",
       "      <td>16.2946</td>\n",
       "      <td>16.2946</td>\n",
       "      <td>--</td>\n",
       "      <td>--</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>--</td>\n",
       "      <td>--</td>\n",
       "      <td>3.441565e+11</td>\n",
       "      <td>3.441565e+11</td>\n",
       "      <td>1.865347e+10</td>\n",
       "      <td>6.801</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>600000.SH</td>\n",
       "      <td>浦发银行</td>\n",
       "      <td>2016-02-24</td>\n",
       "      <td>16.2946</td>\n",
       "      <td>16.2946</td>\n",
       "      <td>16.2946</td>\n",
       "      <td>16.2946</td>\n",
       "      <td>16.2946</td>\n",
       "      <td>--</td>\n",
       "      <td>--</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>--</td>\n",
       "      <td>--</td>\n",
       "      <td>3.441565e+11</td>\n",
       "      <td>3.441565e+11</td>\n",
       "      <td>1.865347e+10</td>\n",
       "      <td>6.801</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>600000.SH</td>\n",
       "      <td>浦发银行</td>\n",
       "      <td>2016-02-25</td>\n",
       "      <td>16.2946</td>\n",
       "      <td>16.2946</td>\n",
       "      <td>16.2946</td>\n",
       "      <td>16.2946</td>\n",
       "      <td>16.2946</td>\n",
       "      <td>--</td>\n",
       "      <td>--</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>--</td>\n",
       "      <td>--</td>\n",
       "      <td>3.441565e+11</td>\n",
       "      <td>3.441565e+11</td>\n",
       "      <td>1.865347e+10</td>\n",
       "      <td>6.801</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>600000.SH</td>\n",
       "      <td>浦发银行</td>\n",
       "      <td>2016-02-26</td>\n",
       "      <td>16.2946</td>\n",
       "      <td>16.2946</td>\n",
       "      <td>16.2946</td>\n",
       "      <td>16.2946</td>\n",
       "      <td>16.2946</td>\n",
       "      <td>--</td>\n",
       "      <td>--</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>--</td>\n",
       "      <td>--</td>\n",
       "      <td>3.441565e+11</td>\n",
       "      <td>3.441565e+11</td>\n",
       "      <td>1.865347e+10</td>\n",
       "      <td>6.801</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>600000.SH</td>\n",
       "      <td>浦发银行</td>\n",
       "      <td>2016-02-29</td>\n",
       "      <td>16.2946</td>\n",
       "      <td>16.2946</td>\n",
       "      <td>16.2946</td>\n",
       "      <td>16.2946</td>\n",
       "      <td>16.2946</td>\n",
       "      <td>--</td>\n",
       "      <td>--</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>--</td>\n",
       "      <td>--</td>\n",
       "      <td>3.441565e+11</td>\n",
       "      <td>3.441565e+11</td>\n",
       "      <td>1.865347e+10</td>\n",
       "      <td>6.801</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>600000.SH</td>\n",
       "      <td>浦发银行</td>\n",
       "      <td>2016-03-01</td>\n",
       "      <td>16.2946</td>\n",
       "      <td>16.2946</td>\n",
       "      <td>16.2946</td>\n",
       "      <td>16.2946</td>\n",
       "      <td>16.2946</td>\n",
       "      <td>--</td>\n",
       "      <td>--</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>--</td>\n",
       "      <td>--</td>\n",
       "      <td>3.441565e+11</td>\n",
       "      <td>3.441565e+11</td>\n",
       "      <td>1.865347e+10</td>\n",
       "      <td>6.801</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>600000.SH</td>\n",
       "      <td>浦发银行</td>\n",
       "      <td>2016-03-02</td>\n",
       "      <td>16.2946</td>\n",
       "      <td>16.2946</td>\n",
       "      <td>16.2946</td>\n",
       "      <td>16.2946</td>\n",
       "      <td>16.2946</td>\n",
       "      <td>--</td>\n",
       "      <td>--</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>--</td>\n",
       "      <td>--</td>\n",
       "      <td>3.441565e+11</td>\n",
       "      <td>3.441565e+11</td>\n",
       "      <td>1.865347e+10</td>\n",
       "      <td>6.801</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>600000.SH</td>\n",
       "      <td>浦发银行</td>\n",
       "      <td>2016-03-03</td>\n",
       "      <td>16.2946</td>\n",
       "      <td>16.2946</td>\n",
       "      <td>16.2946</td>\n",
       "      <td>16.2946</td>\n",
       "      <td>16.2946</td>\n",
       "      <td>--</td>\n",
       "      <td>--</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>--</td>\n",
       "      <td>--</td>\n",
       "      <td>3.441565e+11</td>\n",
       "      <td>3.441565e+11</td>\n",
       "      <td>1.865347e+10</td>\n",
       "      <td>6.801</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>600000.SH</td>\n",
       "      <td>浦发银行</td>\n",
       "      <td>2016-03-04</td>\n",
       "      <td>16.2946</td>\n",
       "      <td>16.2946</td>\n",
       "      <td>16.2946</td>\n",
       "      <td>16.2946</td>\n",
       "      <td>16.2946</td>\n",
       "      <td>--</td>\n",
       "      <td>--</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>--</td>\n",
       "      <td>--</td>\n",
       "      <td>3.441565e+11</td>\n",
       "      <td>3.441565e+11</td>\n",
       "      <td>1.865347e+10</td>\n",
       "      <td>6.801</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>600000.SH</td>\n",
       "      <td>浦发银行</td>\n",
       "      <td>2016-03-07</td>\n",
       "      <td>16.2946</td>\n",
       "      <td>16.2946</td>\n",
       "      <td>16.2946</td>\n",
       "      <td>16.2946</td>\n",
       "      <td>16.2946</td>\n",
       "      <td>--</td>\n",
       "      <td>--</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>--</td>\n",
       "      <td>--</td>\n",
       "      <td>3.441565e+11</td>\n",
       "      <td>3.441565e+11</td>\n",
       "      <td>1.865347e+10</td>\n",
       "      <td>6.801</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>600000.SH</td>\n",
       "      <td>浦发银行</td>\n",
       "      <td>2016-03-08</td>\n",
       "      <td>16.2946</td>\n",
       "      <td>16.2946</td>\n",
       "      <td>16.2946</td>\n",
       "      <td>16.2946</td>\n",
       "      <td>16.2946</td>\n",
       "      <td>--</td>\n",
       "      <td>--</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>--</td>\n",
       "      <td>--</td>\n",
       "      <td>3.441565e+11</td>\n",
       "      <td>3.441565e+11</td>\n",
       "      <td>1.865347e+10</td>\n",
       "      <td>6.801</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>600000.SH</td>\n",
       "      <td>浦发银行</td>\n",
       "      <td>2016-03-09</td>\n",
       "      <td>16.2946</td>\n",
       "      <td>16.2946</td>\n",
       "      <td>16.2946</td>\n",
       "      <td>16.2946</td>\n",
       "      <td>16.2946</td>\n",
       "      <td>--</td>\n",
       "      <td>--</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>--</td>\n",
       "      <td>--</td>\n",
       "      <td>3.441565e+11</td>\n",
       "      <td>3.441565e+11</td>\n",
       "      <td>1.865347e+10</td>\n",
       "      <td>6.801</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>600000.SH</td>\n",
       "      <td>浦发银行</td>\n",
       "      <td>2016-03-10</td>\n",
       "      <td>16.2946</td>\n",
       "      <td>16.2946</td>\n",
       "      <td>16.2946</td>\n",
       "      <td>16.2946</td>\n",
       "      <td>16.2946</td>\n",
       "      <td>--</td>\n",
       "      <td>--</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>--</td>\n",
       "      <td>--</td>\n",
       "      <td>3.441565e+11</td>\n",
       "      <td>3.441565e+11</td>\n",
       "      <td>1.865347e+10</td>\n",
       "      <td>6.801</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           代码    简称         日期  前收盘价(元)   开盘价(元)   最高价(元)   最低价(元)   收盘价(元)  \\\n",
       "26  600000.SH  浦发银行 2016-02-16  16.2946  16.2946  16.2946  16.2946  16.2946   \n",
       "27  600000.SH  浦发银行 2016-02-17  16.2946  16.2946  16.2946  16.2946  16.2946   \n",
       "28  600000.SH  浦发银行 2016-02-18  16.2946  16.2946  16.2946  16.2946  16.2946   \n",
       "29  600000.SH  浦发银行 2016-02-19  16.2946  16.2946  16.2946  16.2946  16.2946   \n",
       "30  600000.SH  浦发银行 2016-02-22  16.2946  16.2946  16.2946  16.2946  16.2946   \n",
       "31  600000.SH  浦发银行 2016-02-23  16.2946  16.2946  16.2946  16.2946  16.2946   \n",
       "32  600000.SH  浦发银行 2016-02-24  16.2946  16.2946  16.2946  16.2946  16.2946   \n",
       "33  600000.SH  浦发银行 2016-02-25  16.2946  16.2946  16.2946  16.2946  16.2946   \n",
       "34  600000.SH  浦发银行 2016-02-26  16.2946  16.2946  16.2946  16.2946  16.2946   \n",
       "35  600000.SH  浦发银行 2016-02-29  16.2946  16.2946  16.2946  16.2946  16.2946   \n",
       "36  600000.SH  浦发银行 2016-03-01  16.2946  16.2946  16.2946  16.2946  16.2946   \n",
       "37  600000.SH  浦发银行 2016-03-02  16.2946  16.2946  16.2946  16.2946  16.2946   \n",
       "38  600000.SH  浦发银行 2016-03-03  16.2946  16.2946  16.2946  16.2946  16.2946   \n",
       "39  600000.SH  浦发银行 2016-03-04  16.2946  16.2946  16.2946  16.2946  16.2946   \n",
       "40  600000.SH  浦发银行 2016-03-07  16.2946  16.2946  16.2946  16.2946  16.2946   \n",
       "41  600000.SH  浦发银行 2016-03-08  16.2946  16.2946  16.2946  16.2946  16.2946   \n",
       "42  600000.SH  浦发银行 2016-03-09  16.2946  16.2946  16.2946  16.2946  16.2946   \n",
       "43  600000.SH  浦发银行 2016-03-10  16.2946  16.2946  16.2946  16.2946  16.2946   \n",
       "\n",
       "   成交量(股) 成交金额(元)  涨跌(元)  涨跌幅(%) 均价(元) 换手率(%)     A股流通市值(元)        总市值(元)  \\\n",
       "26     --      --    0.0     0.0    --     --  3.441565e+11  3.441565e+11   \n",
       "27     --      --    0.0     0.0    --     --  3.441565e+11  3.441565e+11   \n",
       "28     --      --    0.0     0.0    --     --  3.441565e+11  3.441565e+11   \n",
       "29     --      --    0.0     0.0    --     --  3.441565e+11  3.441565e+11   \n",
       "30     --      --    0.0     0.0    --     --  3.441565e+11  3.441565e+11   \n",
       "31     --      --    0.0     0.0    --     --  3.441565e+11  3.441565e+11   \n",
       "32     --      --    0.0     0.0    --     --  3.441565e+11  3.441565e+11   \n",
       "33     --      --    0.0     0.0    --     --  3.441565e+11  3.441565e+11   \n",
       "34     --      --    0.0     0.0    --     --  3.441565e+11  3.441565e+11   \n",
       "35     --      --    0.0     0.0    --     --  3.441565e+11  3.441565e+11   \n",
       "36     --      --    0.0     0.0    --     --  3.441565e+11  3.441565e+11   \n",
       "37     --      --    0.0     0.0    --     --  3.441565e+11  3.441565e+11   \n",
       "38     --      --    0.0     0.0    --     --  3.441565e+11  3.441565e+11   \n",
       "39     --      --    0.0     0.0    --     --  3.441565e+11  3.441565e+11   \n",
       "40     --      --    0.0     0.0    --     --  3.441565e+11  3.441565e+11   \n",
       "41     --      --    0.0     0.0    --     --  3.441565e+11  3.441565e+11   \n",
       "42     --      --    0.0     0.0    --     --  3.441565e+11  3.441565e+11   \n",
       "43     --      --    0.0     0.0    --     --  3.441565e+11  3.441565e+11   \n",
       "\n",
       "       A股流通股本(股)    市盈率  \n",
       "26  1.865347e+10  6.801  \n",
       "27  1.865347e+10  6.801  \n",
       "28  1.865347e+10  6.801  \n",
       "29  1.865347e+10  6.801  \n",
       "30  1.865347e+10  6.801  \n",
       "31  1.865347e+10  6.801  \n",
       "32  1.865347e+10  6.801  \n",
       "33  1.865347e+10  6.801  \n",
       "34  1.865347e+10  6.801  \n",
       "35  1.865347e+10  6.801  \n",
       "36  1.865347e+10  6.801  \n",
       "37  1.865347e+10  6.801  \n",
       "38  1.865347e+10  6.801  \n",
       "39  1.865347e+10  6.801  \n",
       "40  1.865347e+10  6.801  \n",
       "41  1.865347e+10  6.801  \n",
       "42  1.865347e+10  6.801  \n",
       "43  1.865347e+10  6.801  "
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 63.打印所有换手率为--的行"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>代码</th>\n",
       "      <th>简称</th>\n",
       "      <th>日期</th>\n",
       "      <th>前收盘价(元)</th>\n",
       "      <th>开盘价(元)</th>\n",
       "      <th>最高价(元)</th>\n",
       "      <th>最低价(元)</th>\n",
       "      <th>收盘价(元)</th>\n",
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       "      <th>成交金额(元)</th>\n",
       "      <th>涨跌(元)</th>\n",
       "      <th>涨跌幅(%)</th>\n",
       "      <th>均价(元)</th>\n",
       "      <th>换手率(%)</th>\n",
       "      <th>A股流通市值(元)</th>\n",
       "      <th>总市值(元)</th>\n",
       "      <th>A股流通股本(股)</th>\n",
       "      <th>市盈率</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>600000.SH</td>\n",
       "      <td>浦发银行</td>\n",
       "      <td>2016-02-16</td>\n",
       "      <td>16.2946</td>\n",
       "      <td>16.2946</td>\n",
       "      <td>16.2946</td>\n",
       "      <td>16.2946</td>\n",
       "      <td>16.2946</td>\n",
       "      <td>--</td>\n",
       "      <td>--</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>--</td>\n",
       "      <td>--</td>\n",
       "      <td>3.441565e+11</td>\n",
       "      <td>3.441565e+11</td>\n",
       "      <td>1.865347e+10</td>\n",
       "      <td>6.801</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>600000.SH</td>\n",
       "      <td>浦发银行</td>\n",
       "      <td>2016-02-17</td>\n",
       "      <td>16.2946</td>\n",
       "      <td>16.2946</td>\n",
       "      <td>16.2946</td>\n",
       "      <td>16.2946</td>\n",
       "      <td>16.2946</td>\n",
       "      <td>--</td>\n",
       "      <td>--</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>--</td>\n",
       "      <td>--</td>\n",
       "      <td>3.441565e+11</td>\n",
       "      <td>3.441565e+11</td>\n",
       "      <td>1.865347e+10</td>\n",
       "      <td>6.801</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>600000.SH</td>\n",
       "      <td>浦发银行</td>\n",
       "      <td>2016-02-18</td>\n",
       "      <td>16.2946</td>\n",
       "      <td>16.2946</td>\n",
       "      <td>16.2946</td>\n",
       "      <td>16.2946</td>\n",
       "      <td>16.2946</td>\n",
       "      <td>--</td>\n",
       "      <td>--</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>--</td>\n",
       "      <td>--</td>\n",
       "      <td>3.441565e+11</td>\n",
       "      <td>3.441565e+11</td>\n",
       "      <td>1.865347e+10</td>\n",
       "      <td>6.801</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>600000.SH</td>\n",
       "      <td>浦发银行</td>\n",
       "      <td>2016-02-19</td>\n",
       "      <td>16.2946</td>\n",
       "      <td>16.2946</td>\n",
       "      <td>16.2946</td>\n",
       "      <td>16.2946</td>\n",
       "      <td>16.2946</td>\n",
       "      <td>--</td>\n",
       "      <td>--</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>--</td>\n",
       "      <td>--</td>\n",
       "      <td>3.441565e+11</td>\n",
       "      <td>3.441565e+11</td>\n",
       "      <td>1.865347e+10</td>\n",
       "      <td>6.801</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>600000.SH</td>\n",
       "      <td>浦发银行</td>\n",
       "      <td>2016-02-22</td>\n",
       "      <td>16.2946</td>\n",
       "      <td>16.2946</td>\n",
       "      <td>16.2946</td>\n",
       "      <td>16.2946</td>\n",
       "      <td>16.2946</td>\n",
       "      <td>--</td>\n",
       "      <td>--</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>--</td>\n",
       "      <td>--</td>\n",
       "      <td>3.441565e+11</td>\n",
       "      <td>3.441565e+11</td>\n",
       "      <td>1.865347e+10</td>\n",
       "      <td>6.801</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>600000.SH</td>\n",
       "      <td>浦发银行</td>\n",
       "      <td>2016-02-23</td>\n",
       "      <td>16.2946</td>\n",
       "      <td>16.2946</td>\n",
       "      <td>16.2946</td>\n",
       "      <td>16.2946</td>\n",
       "      <td>16.2946</td>\n",
       "      <td>--</td>\n",
       "      <td>--</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>--</td>\n",
       "      <td>--</td>\n",
       "      <td>3.441565e+11</td>\n",
       "      <td>3.441565e+11</td>\n",
       "      <td>1.865347e+10</td>\n",
       "      <td>6.801</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>600000.SH</td>\n",
       "      <td>浦发银行</td>\n",
       "      <td>2016-02-24</td>\n",
       "      <td>16.2946</td>\n",
       "      <td>16.2946</td>\n",
       "      <td>16.2946</td>\n",
       "      <td>16.2946</td>\n",
       "      <td>16.2946</td>\n",
       "      <td>--</td>\n",
       "      <td>--</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>--</td>\n",
       "      <td>--</td>\n",
       "      <td>3.441565e+11</td>\n",
       "      <td>3.441565e+11</td>\n",
       "      <td>1.865347e+10</td>\n",
       "      <td>6.801</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>600000.SH</td>\n",
       "      <td>浦发银行</td>\n",
       "      <td>2016-02-25</td>\n",
       "      <td>16.2946</td>\n",
       "      <td>16.2946</td>\n",
       "      <td>16.2946</td>\n",
       "      <td>16.2946</td>\n",
       "      <td>16.2946</td>\n",
       "      <td>--</td>\n",
       "      <td>--</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>--</td>\n",
       "      <td>--</td>\n",
       "      <td>3.441565e+11</td>\n",
       "      <td>3.441565e+11</td>\n",
       "      <td>1.865347e+10</td>\n",
       "      <td>6.801</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>600000.SH</td>\n",
       "      <td>浦发银行</td>\n",
       "      <td>2016-02-26</td>\n",
       "      <td>16.2946</td>\n",
       "      <td>16.2946</td>\n",
       "      <td>16.2946</td>\n",
       "      <td>16.2946</td>\n",
       "      <td>16.2946</td>\n",
       "      <td>--</td>\n",
       "      <td>--</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>--</td>\n",
       "      <td>--</td>\n",
       "      <td>3.441565e+11</td>\n",
       "      <td>3.441565e+11</td>\n",
       "      <td>1.865347e+10</td>\n",
       "      <td>6.801</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>600000.SH</td>\n",
       "      <td>浦发银行</td>\n",
       "      <td>2016-02-29</td>\n",
       "      <td>16.2946</td>\n",
       "      <td>16.2946</td>\n",
       "      <td>16.2946</td>\n",
       "      <td>16.2946</td>\n",
       "      <td>16.2946</td>\n",
       "      <td>--</td>\n",
       "      <td>--</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>--</td>\n",
       "      <td>--</td>\n",
       "      <td>3.441565e+11</td>\n",
       "      <td>3.441565e+11</td>\n",
       "      <td>1.865347e+10</td>\n",
       "      <td>6.801</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>600000.SH</td>\n",
       "      <td>浦发银行</td>\n",
       "      <td>2016-03-01</td>\n",
       "      <td>16.2946</td>\n",
       "      <td>16.2946</td>\n",
       "      <td>16.2946</td>\n",
       "      <td>16.2946</td>\n",
       "      <td>16.2946</td>\n",
       "      <td>--</td>\n",
       "      <td>--</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>--</td>\n",
       "      <td>--</td>\n",
       "      <td>3.441565e+11</td>\n",
       "      <td>3.441565e+11</td>\n",
       "      <td>1.865347e+10</td>\n",
       "      <td>6.801</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>600000.SH</td>\n",
       "      <td>浦发银行</td>\n",
       "      <td>2016-03-02</td>\n",
       "      <td>16.2946</td>\n",
       "      <td>16.2946</td>\n",
       "      <td>16.2946</td>\n",
       "      <td>16.2946</td>\n",
       "      <td>16.2946</td>\n",
       "      <td>--</td>\n",
       "      <td>--</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>--</td>\n",
       "      <td>--</td>\n",
       "      <td>3.441565e+11</td>\n",
       "      <td>3.441565e+11</td>\n",
       "      <td>1.865347e+10</td>\n",
       "      <td>6.801</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>600000.SH</td>\n",
       "      <td>浦发银行</td>\n",
       "      <td>2016-03-03</td>\n",
       "      <td>16.2946</td>\n",
       "      <td>16.2946</td>\n",
       "      <td>16.2946</td>\n",
       "      <td>16.2946</td>\n",
       "      <td>16.2946</td>\n",
       "      <td>--</td>\n",
       "      <td>--</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>--</td>\n",
       "      <td>--</td>\n",
       "      <td>3.441565e+11</td>\n",
       "      <td>3.441565e+11</td>\n",
       "      <td>1.865347e+10</td>\n",
       "      <td>6.801</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>600000.SH</td>\n",
       "      <td>浦发银行</td>\n",
       "      <td>2016-03-04</td>\n",
       "      <td>16.2946</td>\n",
       "      <td>16.2946</td>\n",
       "      <td>16.2946</td>\n",
       "      <td>16.2946</td>\n",
       "      <td>16.2946</td>\n",
       "      <td>--</td>\n",
       "      <td>--</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>--</td>\n",
       "      <td>--</td>\n",
       "      <td>3.441565e+11</td>\n",
       "      <td>3.441565e+11</td>\n",
       "      <td>1.865347e+10</td>\n",
       "      <td>6.801</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>600000.SH</td>\n",
       "      <td>浦发银行</td>\n",
       "      <td>2016-03-07</td>\n",
       "      <td>16.2946</td>\n",
       "      <td>16.2946</td>\n",
       "      <td>16.2946</td>\n",
       "      <td>16.2946</td>\n",
       "      <td>16.2946</td>\n",
       "      <td>--</td>\n",
       "      <td>--</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>--</td>\n",
       "      <td>--</td>\n",
       "      <td>3.441565e+11</td>\n",
       "      <td>3.441565e+11</td>\n",
       "      <td>1.865347e+10</td>\n",
       "      <td>6.801</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>600000.SH</td>\n",
       "      <td>浦发银行</td>\n",
       "      <td>2016-03-08</td>\n",
       "      <td>16.2946</td>\n",
       "      <td>16.2946</td>\n",
       "      <td>16.2946</td>\n",
       "      <td>16.2946</td>\n",
       "      <td>16.2946</td>\n",
       "      <td>--</td>\n",
       "      <td>--</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>--</td>\n",
       "      <td>--</td>\n",
       "      <td>3.441565e+11</td>\n",
       "      <td>3.441565e+11</td>\n",
       "      <td>1.865347e+10</td>\n",
       "      <td>6.801</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>600000.SH</td>\n",
       "      <td>浦发银行</td>\n",
       "      <td>2016-03-09</td>\n",
       "      <td>16.2946</td>\n",
       "      <td>16.2946</td>\n",
       "      <td>16.2946</td>\n",
       "      <td>16.2946</td>\n",
       "      <td>16.2946</td>\n",
       "      <td>--</td>\n",
       "      <td>--</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>--</td>\n",
       "      <td>--</td>\n",
       "      <td>3.441565e+11</td>\n",
       "      <td>3.441565e+11</td>\n",
       "      <td>1.865347e+10</td>\n",
       "      <td>6.801</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>600000.SH</td>\n",
       "      <td>浦发银行</td>\n",
       "      <td>2016-03-10</td>\n",
       "      <td>16.2946</td>\n",
       "      <td>16.2946</td>\n",
       "      <td>16.2946</td>\n",
       "      <td>16.2946</td>\n",
       "      <td>16.2946</td>\n",
       "      <td>--</td>\n",
       "      <td>--</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>--</td>\n",
       "      <td>--</td>\n",
       "      <td>3.441565e+11</td>\n",
       "      <td>3.441565e+11</td>\n",
       "      <td>1.865347e+10</td>\n",
       "      <td>6.801</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           代码    简称         日期  前收盘价(元)   开盘价(元)   最高价(元)   最低价(元)   收盘价(元)  \\\n",
       "26  600000.SH  浦发银行 2016-02-16  16.2946  16.2946  16.2946  16.2946  16.2946   \n",
       "27  600000.SH  浦发银行 2016-02-17  16.2946  16.2946  16.2946  16.2946  16.2946   \n",
       "28  600000.SH  浦发银行 2016-02-18  16.2946  16.2946  16.2946  16.2946  16.2946   \n",
       "29  600000.SH  浦发银行 2016-02-19  16.2946  16.2946  16.2946  16.2946  16.2946   \n",
       "30  600000.SH  浦发银行 2016-02-22  16.2946  16.2946  16.2946  16.2946  16.2946   \n",
       "31  600000.SH  浦发银行 2016-02-23  16.2946  16.2946  16.2946  16.2946  16.2946   \n",
       "32  600000.SH  浦发银行 2016-02-24  16.2946  16.2946  16.2946  16.2946  16.2946   \n",
       "33  600000.SH  浦发银行 2016-02-25  16.2946  16.2946  16.2946  16.2946  16.2946   \n",
       "34  600000.SH  浦发银行 2016-02-26  16.2946  16.2946  16.2946  16.2946  16.2946   \n",
       "35  600000.SH  浦发银行 2016-02-29  16.2946  16.2946  16.2946  16.2946  16.2946   \n",
       "36  600000.SH  浦发银行 2016-03-01  16.2946  16.2946  16.2946  16.2946  16.2946   \n",
       "37  600000.SH  浦发银行 2016-03-02  16.2946  16.2946  16.2946  16.2946  16.2946   \n",
       "38  600000.SH  浦发银行 2016-03-03  16.2946  16.2946  16.2946  16.2946  16.2946   \n",
       "39  600000.SH  浦发银行 2016-03-04  16.2946  16.2946  16.2946  16.2946  16.2946   \n",
       "40  600000.SH  浦发银行 2016-03-07  16.2946  16.2946  16.2946  16.2946  16.2946   \n",
       "41  600000.SH  浦发银行 2016-03-08  16.2946  16.2946  16.2946  16.2946  16.2946   \n",
       "42  600000.SH  浦发银行 2016-03-09  16.2946  16.2946  16.2946  16.2946  16.2946   \n",
       "43  600000.SH  浦发银行 2016-03-10  16.2946  16.2946  16.2946  16.2946  16.2946   \n",
       "\n",
       "   成交量(股) 成交金额(元)  涨跌(元)  涨跌幅(%) 均价(元) 换手率(%)     A股流通市值(元)        总市值(元)  \\\n",
       "26     --      --    0.0     0.0    --     --  3.441565e+11  3.441565e+11   \n",
       "27     --      --    0.0     0.0    --     --  3.441565e+11  3.441565e+11   \n",
       "28     --      --    0.0     0.0    --     --  3.441565e+11  3.441565e+11   \n",
       "29     --      --    0.0     0.0    --     --  3.441565e+11  3.441565e+11   \n",
       "30     --      --    0.0     0.0    --     --  3.441565e+11  3.441565e+11   \n",
       "31     --      --    0.0     0.0    --     --  3.441565e+11  3.441565e+11   \n",
       "32     --      --    0.0     0.0    --     --  3.441565e+11  3.441565e+11   \n",
       "33     --      --    0.0     0.0    --     --  3.441565e+11  3.441565e+11   \n",
       "34     --      --    0.0     0.0    --     --  3.441565e+11  3.441565e+11   \n",
       "35     --      --    0.0     0.0    --     --  3.441565e+11  3.441565e+11   \n",
       "36     --      --    0.0     0.0    --     --  3.441565e+11  3.441565e+11   \n",
       "37     --      --    0.0     0.0    --     --  3.441565e+11  3.441565e+11   \n",
       "38     --      --    0.0     0.0    --     --  3.441565e+11  3.441565e+11   \n",
       "39     --      --    0.0     0.0    --     --  3.441565e+11  3.441565e+11   \n",
       "40     --      --    0.0     0.0    --     --  3.441565e+11  3.441565e+11   \n",
       "41     --      --    0.0     0.0    --     --  3.441565e+11  3.441565e+11   \n",
       "42     --      --    0.0     0.0    --     --  3.441565e+11  3.441565e+11   \n",
       "43     --      --    0.0     0.0    --     --  3.441565e+11  3.441565e+11   \n",
       "\n",
       "       A股流通股本(股)    市盈率  \n",
       "26  1.865347e+10  6.801  \n",
       "27  1.865347e+10  6.801  \n",
       "28  1.865347e+10  6.801  \n",
       "29  1.865347e+10  6.801  \n",
       "30  1.865347e+10  6.801  \n",
       "31  1.865347e+10  6.801  \n",
       "32  1.865347e+10  6.801  \n",
       "33  1.865347e+10  6.801  \n",
       "34  1.865347e+10  6.801  \n",
       "35  1.865347e+10  6.801  \n",
       "36  1.865347e+10  6.801  \n",
       "37  1.865347e+10  6.801  \n",
       "38  1.865347e+10  6.801  \n",
       "39  1.865347e+10  6.801  \n",
       "40  1.865347e+10  6.801  \n",
       "41  1.865347e+10  6.801  \n",
       "42  1.865347e+10  6.801  \n",
       "43  1.865347e+10  6.801  "
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 64.重置data的行号"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 65.删除所有换手率为非数字的行"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 66.绘制换手率的密度曲线"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7ffb08fae490>"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 600x450 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 67.计算前一天与后一天收盘价的差值"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0         NaN\n",
       "1      0.1413\n",
       "2      0.1237\n",
       "3     -0.5211\n",
       "4     -0.0177\n",
       "        ...  \n",
       "322   -0.0800\n",
       "323   -0.1000\n",
       "324   -0.0600\n",
       "325   -0.0600\n",
       "326   -0.1000\n",
       "Name: 收盘价(元), Length: 309, dtype: float64"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 68.计算前一天与后一天收盘价变化率"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0           NaN\n",
       "1      0.008988\n",
       "2      0.007799\n",
       "3     -0.032598\n",
       "4     -0.001145\n",
       "         ...   \n",
       "322   -0.005277\n",
       "323   -0.006631\n",
       "324   -0.004005\n",
       "325   -0.004021\n",
       "326   -0.006729\n",
       "Name: 收盘价(元), Length: 309, dtype: float64"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 69.设置日期为索引"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 70.以5个数据作为一个数据滑动窗口，在这个5个数据上取均值(收盘价)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "日期\n",
       "2016-01-04         NaN\n",
       "2016-01-05         NaN\n",
       "2016-01-06         NaN\n",
       "2016-01-07         NaN\n",
       "2016-01-08    15.69578\n",
       "                ...   \n",
       "2017-05-03    15.14200\n",
       "2017-05-04    15.12800\n",
       "2017-05-05    15.07000\n",
       "2017-05-08    15.00000\n",
       "2017-05-09    14.92000\n",
       "Name: 收盘价(元), Length: 309, dtype: float64"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 71.以5个数据作为一个数据滑动窗口，计算这五个数据总和(收盘价)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "日期\n",
       "2016-01-04        NaN\n",
       "2016-01-05        NaN\n",
       "2016-01-06        NaN\n",
       "2016-01-07        NaN\n",
       "2016-01-08    78.4789\n",
       "               ...   \n",
       "2017-05-03    75.7100\n",
       "2017-05-04    75.6400\n",
       "2017-05-05    75.3500\n",
       "2017-05-08    75.0000\n",
       "2017-05-09    74.6000\n",
       "Name: 收盘价(元), Length: 309, dtype: float64"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 72.将收盘价5日均线、20日均线与原始数据绘制在同一个图上"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7ffb09227bd0>"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:214: RuntimeWarning: Glyph 26085 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "/opt/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:214: RuntimeWarning: Glyph 26399 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "/opt/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:183: RuntimeWarning: Glyph 26085 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "/opt/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:183: RuntimeWarning: Glyph 26399 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 600x450 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 73.按周为采样规则，取一周收盘价最大值"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "日期\n",
       "2016-01-10    15.9855\n",
       "2016-01-17    15.8265\n",
       "2016-01-24    15.6940\n",
       "2016-01-31    15.0405\n",
       "2016-02-07    16.2328\n",
       "               ...   \n",
       "2017-04-16    15.9700\n",
       "2017-04-23    15.5600\n",
       "2017-04-30    15.2100\n",
       "2017-05-07    15.1600\n",
       "2017-05-14    14.8600\n",
       "Freq: W-SUN, Name: 收盘价(元), Length: 71, dtype: float64"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 74.绘制重采样数据与原始数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7ffb094e3e50>"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:214: RuntimeWarning: Glyph 26085 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "/opt/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:214: RuntimeWarning: Glyph 26399 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "/opt/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:183: RuntimeWarning: Glyph 26085 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "/opt/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:183: RuntimeWarning: Glyph 26399 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 600x450 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 75.将数据往后移动5天"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>index</th>\n",
       "      <th>代码</th>\n",
       "      <th>简称</th>\n",
       "      <th>前收盘价(元)</th>\n",
       "      <th>开盘价(元)</th>\n",
       "      <th>最高价(元)</th>\n",
       "      <th>最低价(元)</th>\n",
       "      <th>收盘价(元)</th>\n",
       "      <th>成交量(股)</th>\n",
       "      <th>成交金额(元)</th>\n",
       "      <th>涨跌(元)</th>\n",
       "      <th>涨跌幅(%)</th>\n",
       "      <th>均价(元)</th>\n",
       "      <th>换手率(%)</th>\n",
       "      <th>A股流通市值(元)</th>\n",
       "      <th>总市值(元)</th>\n",
       "      <th>A股流通股本(股)</th>\n",
       "      <th>市盈率</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>日期</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2016-01-04</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-05</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-06</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-07</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-08</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-05-03</th>\n",
       "      <td>317.0</td>\n",
       "      <td>600000.SH</td>\n",
       "      <td>浦发银行</td>\n",
       "      <td>15.00</td>\n",
       "      <td>15.02</td>\n",
       "      <td>15.10</td>\n",
       "      <td>14.99</td>\n",
       "      <td>15.05</td>\n",
       "      <td>12975919</td>\n",
       "      <td>195296862</td>\n",
       "      <td>0.05</td>\n",
       "      <td>0.3333</td>\n",
       "      <td>15.0507</td>\n",
       "      <td>0.06</td>\n",
       "      <td>3.253551e+11</td>\n",
       "      <td>3.253551e+11</td>\n",
       "      <td>2.161828e+10</td>\n",
       "      <td>6.1273</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-05-04</th>\n",
       "      <td>318.0</td>\n",
       "      <td>600000.SH</td>\n",
       "      <td>浦发银行</td>\n",
       "      <td>15.05</td>\n",
       "      <td>15.06</td>\n",
       "      <td>15.11</td>\n",
       "      <td>15.00</td>\n",
       "      <td>15.05</td>\n",
       "      <td>14939871</td>\n",
       "      <td>225022668</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>15.0619</td>\n",
       "      <td>0.0691</td>\n",
       "      <td>3.253551e+11</td>\n",
       "      <td>3.253551e+11</td>\n",
       "      <td>2.161828e+10</td>\n",
       "      <td>6.1273</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-05-05</th>\n",
       "      <td>319.0</td>\n",
       "      <td>600000.SH</td>\n",
       "      <td>浦发银行</td>\n",
       "      <td>15.05</td>\n",
       "      <td>15.05</td>\n",
       "      <td>15.25</td>\n",
       "      <td>15.03</td>\n",
       "      <td>15.21</td>\n",
       "      <td>22887645</td>\n",
       "      <td>345791526</td>\n",
       "      <td>0.16</td>\n",
       "      <td>1.0631</td>\n",
       "      <td>15.1082</td>\n",
       "      <td>0.1059</td>\n",
       "      <td>3.288140e+11</td>\n",
       "      <td>3.288140e+11</td>\n",
       "      <td>2.161828e+10</td>\n",
       "      <td>6.1925</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-05-08</th>\n",
       "      <td>320.0</td>\n",
       "      <td>600000.SH</td>\n",
       "      <td>浦发银行</td>\n",
       "      <td>15.21</td>\n",
       "      <td>15.15</td>\n",
       "      <td>15.22</td>\n",
       "      <td>15.08</td>\n",
       "      <td>15.21</td>\n",
       "      <td>15718509</td>\n",
       "      <td>238419161</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>15.1681</td>\n",
       "      <td>0.0727</td>\n",
       "      <td>3.288140e+11</td>\n",
       "      <td>3.288140e+11</td>\n",
       "      <td>2.161828e+10</td>\n",
       "      <td>6.1925</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-05-09</th>\n",
       "      <td>321.0</td>\n",
       "      <td>600000.SH</td>\n",
       "      <td>浦发银行</td>\n",
       "      <td>15.21</td>\n",
       "      <td>15.21</td>\n",
       "      <td>15.22</td>\n",
       "      <td>15.13</td>\n",
       "      <td>15.16</td>\n",
       "      <td>12607509</td>\n",
       "      <td>191225527</td>\n",
       "      <td>-0.05</td>\n",
       "      <td>-0.3287</td>\n",
       "      <td>15.1676</td>\n",
       "      <td>0.0583</td>\n",
       "      <td>3.277331e+11</td>\n",
       "      <td>3.277331e+11</td>\n",
       "      <td>2.161828e+10</td>\n",
       "      <td>6.1721</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>309 rows × 18 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            index         代码    简称  前收盘价(元)  开盘价(元)  最高价(元)  最低价(元)  收盘价(元)  \\\n",
       "日期                                                                            \n",
       "2016-01-04    NaN        NaN   NaN      NaN     NaN     NaN     NaN     NaN   \n",
       "2016-01-05    NaN        NaN   NaN      NaN     NaN     NaN     NaN     NaN   \n",
       "2016-01-06    NaN        NaN   NaN      NaN     NaN     NaN     NaN     NaN   \n",
       "2016-01-07    NaN        NaN   NaN      NaN     NaN     NaN     NaN     NaN   \n",
       "2016-01-08    NaN        NaN   NaN      NaN     NaN     NaN     NaN     NaN   \n",
       "...           ...        ...   ...      ...     ...     ...     ...     ...   \n",
       "2017-05-03  317.0  600000.SH  浦发银行    15.00   15.02   15.10   14.99   15.05   \n",
       "2017-05-04  318.0  600000.SH  浦发银行    15.05   15.06   15.11   15.00   15.05   \n",
       "2017-05-05  319.0  600000.SH  浦发银行    15.05   15.05   15.25   15.03   15.21   \n",
       "2017-05-08  320.0  600000.SH  浦发银行    15.21   15.15   15.22   15.08   15.21   \n",
       "2017-05-09  321.0  600000.SH  浦发银行    15.21   15.21   15.22   15.13   15.16   \n",
       "\n",
       "              成交量(股)    成交金额(元)  涨跌(元)  涨跌幅(%)    均价(元)  换手率(%)     A股流通市值(元)  \\\n",
       "日期                                                                              \n",
       "2016-01-04       NaN        NaN    NaN     NaN      NaN     NaN           NaN   \n",
       "2016-01-05       NaN        NaN    NaN     NaN      NaN     NaN           NaN   \n",
       "2016-01-06       NaN        NaN    NaN     NaN      NaN     NaN           NaN   \n",
       "2016-01-07       NaN        NaN    NaN     NaN      NaN     NaN           NaN   \n",
       "2016-01-08       NaN        NaN    NaN     NaN      NaN     NaN           NaN   \n",
       "...              ...        ...    ...     ...      ...     ...           ...   \n",
       "2017-05-03  12975919  195296862   0.05  0.3333  15.0507    0.06  3.253551e+11   \n",
       "2017-05-04  14939871  225022668   0.00  0.0000  15.0619  0.0691  3.253551e+11   \n",
       "2017-05-05  22887645  345791526   0.16  1.0631  15.1082  0.1059  3.288140e+11   \n",
       "2017-05-08  15718509  238419161   0.00  0.0000  15.1681  0.0727  3.288140e+11   \n",
       "2017-05-09  12607509  191225527  -0.05 -0.3287  15.1676  0.0583  3.277331e+11   \n",
       "\n",
       "                  总市值(元)     A股流通股本(股)     市盈率  \n",
       "日期                                              \n",
       "2016-01-04           NaN           NaN     NaN  \n",
       "2016-01-05           NaN           NaN     NaN  \n",
       "2016-01-06           NaN           NaN     NaN  \n",
       "2016-01-07           NaN           NaN     NaN  \n",
       "2016-01-08           NaN           NaN     NaN  \n",
       "...                  ...           ...     ...  \n",
       "2017-05-03  3.253551e+11  2.161828e+10  6.1273  \n",
       "2017-05-04  3.253551e+11  2.161828e+10  6.1273  \n",
       "2017-05-05  3.288140e+11  2.161828e+10  6.1925  \n",
       "2017-05-08  3.288140e+11  2.161828e+10  6.1925  \n",
       "2017-05-09  3.277331e+11  2.161828e+10  6.1721  \n",
       "\n",
       "[309 rows x 18 columns]"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 76.将数据向前移动5天"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>index</th>\n",
       "      <th>代码</th>\n",
       "      <th>简称</th>\n",
       "      <th>前收盘价(元)</th>\n",
       "      <th>开盘价(元)</th>\n",
       "      <th>最高价(元)</th>\n",
       "      <th>最低价(元)</th>\n",
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       "      <th>成交量(股)</th>\n",
       "      <th>成交金额(元)</th>\n",
       "      <th>涨跌(元)</th>\n",
       "      <th>涨跌幅(%)</th>\n",
       "      <th>均价(元)</th>\n",
       "      <th>换手率(%)</th>\n",
       "      <th>A股流通市值(元)</th>\n",
       "      <th>总市值(元)</th>\n",
       "      <th>A股流通股本(股)</th>\n",
       "      <th>市盈率</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>日期</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2016-01-04</th>\n",
       "      <td>5.0</td>\n",
       "      <td>600000.SH</td>\n",
       "      <td>浦发银行</td>\n",
       "      <td>15.4467</td>\n",
       "      <td>15.1994</td>\n",
       "      <td>15.4114</td>\n",
       "      <td>14.9786</td>\n",
       "      <td>15.0581</td>\n",
       "      <td>90177135</td>\n",
       "      <td>1550155933</td>\n",
       "      <td>-0.3886</td>\n",
       "      <td>-2.5157</td>\n",
       "      <td>17.1901</td>\n",
       "      <td>0.4834</td>\n",
       "      <td>3.180417e+11</td>\n",
       "      <td>3.180417e+11</td>\n",
       "      <td>1.865347e+10</td>\n",
       "      <td>6.2849</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-05</th>\n",
       "      <td>6.0</td>\n",
       "      <td>600000.SH</td>\n",
       "      <td>浦发银行</td>\n",
       "      <td>15.0581</td>\n",
       "      <td>15.1641</td>\n",
       "      <td>15.4732</td>\n",
       "      <td>15.0846</td>\n",
       "      <td>15.4114</td>\n",
       "      <td>55374454</td>\n",
       "      <td>964061502</td>\n",
       "      <td>0.3533</td>\n",
       "      <td>2.3460</td>\n",
       "      <td>17.4099</td>\n",
       "      <td>0.2969</td>\n",
       "      <td>3.255031e+11</td>\n",
       "      <td>3.255031e+11</td>\n",
       "      <td>1.865347e+10</td>\n",
       "      <td>6.4324</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-06</th>\n",
       "      <td>7.0</td>\n",
       "      <td>600000.SH</td>\n",
       "      <td>浦发银行</td>\n",
       "      <td>15.4114</td>\n",
       "      <td>15.5174</td>\n",
       "      <td>15.8088</td>\n",
       "      <td>15.3231</td>\n",
       "      <td>15.3584</td>\n",
       "      <td>47869312</td>\n",
       "      <td>843717365</td>\n",
       "      <td>-0.0530</td>\n",
       "      <td>-0.3438</td>\n",
       "      <td>17.6254</td>\n",
       "      <td>0.2566</td>\n",
       "      <td>3.243839e+11</td>\n",
       "      <td>3.243839e+11</td>\n",
       "      <td>1.865347e+10</td>\n",
       "      <td>6.4102</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-07</th>\n",
       "      <td>8.0</td>\n",
       "      <td>600000.SH</td>\n",
       "      <td>浦发银行</td>\n",
       "      <td>15.3584</td>\n",
       "      <td>15.0140</td>\n",
       "      <td>15.8883</td>\n",
       "      <td>14.9168</td>\n",
       "      <td>15.8265</td>\n",
       "      <td>54838833</td>\n",
       "      <td>966117848</td>\n",
       "      <td>0.4681</td>\n",
       "      <td>3.0477</td>\n",
       "      <td>17.6174</td>\n",
       "      <td>0.294</td>\n",
       "      <td>3.342702e+11</td>\n",
       "      <td>3.342702e+11</td>\n",
       "      <td>1.865347e+10</td>\n",
       "      <td>6.6056</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-08</th>\n",
       "      <td>9.0</td>\n",
       "      <td>600000.SH</td>\n",
       "      <td>浦发银行</td>\n",
       "      <td>15.8265</td>\n",
       "      <td>15.7205</td>\n",
       "      <td>16.0296</td>\n",
       "      <td>15.4732</td>\n",
       "      <td>15.5262</td>\n",
       "      <td>46723139</td>\n",
       "      <td>836146426</td>\n",
       "      <td>-0.3003</td>\n",
       "      <td>-1.8973</td>\n",
       "      <td>17.8958</td>\n",
       "      <td>0.2505</td>\n",
       "      <td>3.279280e+11</td>\n",
       "      <td>3.279280e+11</td>\n",
       "      <td>1.865347e+10</td>\n",
       "      <td>6.4803</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-05-03</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-05-04</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-05-05</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-05-08</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-05-09</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>309 rows × 18 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            index         代码    简称  前收盘价(元)   开盘价(元)   最高价(元)   最低价(元)  \\\n",
       "日期                                                                       \n",
       "2016-01-04    5.0  600000.SH  浦发银行  15.4467  15.1994  15.4114  14.9786   \n",
       "2016-01-05    6.0  600000.SH  浦发银行  15.0581  15.1641  15.4732  15.0846   \n",
       "2016-01-06    7.0  600000.SH  浦发银行  15.4114  15.5174  15.8088  15.3231   \n",
       "2016-01-07    8.0  600000.SH  浦发银行  15.3584  15.0140  15.8883  14.9168   \n",
       "2016-01-08    9.0  600000.SH  浦发银行  15.8265  15.7205  16.0296  15.4732   \n",
       "...           ...        ...   ...      ...      ...      ...      ...   \n",
       "2017-05-03    NaN        NaN   NaN      NaN      NaN      NaN      NaN   \n",
       "2017-05-04    NaN        NaN   NaN      NaN      NaN      NaN      NaN   \n",
       "2017-05-05    NaN        NaN   NaN      NaN      NaN      NaN      NaN   \n",
       "2017-05-08    NaN        NaN   NaN      NaN      NaN      NaN      NaN   \n",
       "2017-05-09    NaN        NaN   NaN      NaN      NaN      NaN      NaN   \n",
       "\n",
       "             收盘价(元)    成交量(股)     成交金额(元)   涨跌(元)  涨跌幅(%)    均价(元)  换手率(%)  \\\n",
       "日期                                                                           \n",
       "2016-01-04  15.0581  90177135  1550155933 -0.3886 -2.5157  17.1901  0.4834   \n",
       "2016-01-05  15.4114  55374454   964061502  0.3533  2.3460  17.4099  0.2969   \n",
       "2016-01-06  15.3584  47869312   843717365 -0.0530 -0.3438  17.6254  0.2566   \n",
       "2016-01-07  15.8265  54838833   966117848  0.4681  3.0477  17.6174   0.294   \n",
       "2016-01-08  15.5262  46723139   836146426 -0.3003 -1.8973  17.8958  0.2505   \n",
       "...             ...       ...         ...     ...     ...      ...     ...   \n",
       "2017-05-03      NaN       NaN         NaN     NaN     NaN      NaN     NaN   \n",
       "2017-05-04      NaN       NaN         NaN     NaN     NaN      NaN     NaN   \n",
       "2017-05-05      NaN       NaN         NaN     NaN     NaN      NaN     NaN   \n",
       "2017-05-08      NaN       NaN         NaN     NaN     NaN      NaN     NaN   \n",
       "2017-05-09      NaN       NaN         NaN     NaN     NaN      NaN     NaN   \n",
       "\n",
       "               A股流通市值(元)        总市值(元)     A股流通股本(股)     市盈率  \n",
       "日期                                                            \n",
       "2016-01-04  3.180417e+11  3.180417e+11  1.865347e+10  6.2849  \n",
       "2016-01-05  3.255031e+11  3.255031e+11  1.865347e+10  6.4324  \n",
       "2016-01-06  3.243839e+11  3.243839e+11  1.865347e+10  6.4102  \n",
       "2016-01-07  3.342702e+11  3.342702e+11  1.865347e+10  6.6056  \n",
       "2016-01-08  3.279280e+11  3.279280e+11  1.865347e+10  6.4803  \n",
       "...                  ...           ...           ...     ...  \n",
       "2017-05-03           NaN           NaN           NaN     NaN  \n",
       "2017-05-04           NaN           NaN           NaN     NaN  \n",
       "2017-05-05           NaN           NaN           NaN     NaN  \n",
       "2017-05-08           NaN           NaN           NaN     NaN  \n",
       "2017-05-09           NaN           NaN           NaN     NaN  \n",
       "\n",
       "[309 rows x 18 columns]"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 77.使用expending函数计算开盘价的移动窗口均值"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "日期\n",
       "2016-01-04    16.144400\n",
       "2016-01-05    15.804400\n",
       "2016-01-06    15.805867\n",
       "2016-01-07    15.784525\n",
       "2016-01-08    15.761120\n",
       "                ...    \n",
       "2017-05-03    16.041489\n",
       "2017-05-04    16.038314\n",
       "2017-05-05    16.034769\n",
       "2017-05-08    16.030695\n",
       "2017-05-09    16.026356\n",
       "Name: 开盘价(元), Length: 309, dtype: float64"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 78.绘制上一题的移动均值与原始数据折线图"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7ffb09539a90>"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:214: RuntimeWarning: Glyph 26085 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "/opt/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:214: RuntimeWarning: Glyph 26399 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "/opt/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:214: RuntimeWarning: Glyph 24320 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "/opt/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:214: RuntimeWarning: Glyph 30424 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "/opt/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:214: RuntimeWarning: Glyph 20215 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "/opt/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:214: RuntimeWarning: Glyph 20803 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "/opt/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:183: RuntimeWarning: Glyph 26085 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "/opt/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:183: RuntimeWarning: Glyph 26399 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "/opt/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:183: RuntimeWarning: Glyph 24320 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "/opt/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:183: RuntimeWarning: Glyph 30424 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "/opt/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:183: RuntimeWarning: Glyph 20215 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "/opt/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:183: RuntimeWarning: Glyph 20803 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n"
     ]
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 2400x900 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 79.计算布林指标"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 80.计算布林线并绘制"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7ffb09e429d0>"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:214: RuntimeWarning: Glyph 26085 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "/opt/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:214: RuntimeWarning: Glyph 26399 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "/opt/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:214: RuntimeWarning: Glyph 25910 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "/opt/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:214: RuntimeWarning: Glyph 30424 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "/opt/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:214: RuntimeWarning: Glyph 20215 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "/opt/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:214: RuntimeWarning: Glyph 20803 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "/opt/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:183: RuntimeWarning: Glyph 26085 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "/opt/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:183: RuntimeWarning: Glyph 26399 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "/opt/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:183: RuntimeWarning: Glyph 25910 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "/opt/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:183: RuntimeWarning: Glyph 30424 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "/opt/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:183: RuntimeWarning: Glyph 20215 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "/opt/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:183: RuntimeWarning: Glyph 20803 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 2400x900 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 第四期 当Pandas遇上NumPy"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 81.导入并查看pandas与numpy版本"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.19.0\n",
      "1.0.5\n"
     ]
    }
   ],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 82.从NumPy数组创建DataFrame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>93</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>77</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>57</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>98</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>66</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>57</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>73</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>93</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     0\n",
       "0   93\n",
       "1   17\n",
       "2   80\n",
       "3   77\n",
       "4   57\n",
       "5   36\n",
       "6   11\n",
       "7   98\n",
       "8   36\n",
       "9   66\n",
       "10  24\n",
       "11   9\n",
       "12  15\n",
       "13  22\n",
       "14  57\n",
       "15  64\n",
       "16  54\n",
       "17  73\n",
       "18  33\n",
       "19  93"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 83.从NumPy数组创建DataFrame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>85</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
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       "8   40\n",
       "9   45\n",
       "10  50\n",
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       "13  65\n",
       "14  70\n",
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       "16  80\n",
       "17  85\n",
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       "19  95"
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     "execution_count": 85,
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   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 84.从NumPy数组创建DataFrame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
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    "scrolled": true
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       "      <th>16</th>\n",
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       "      <th>17</th>\n",
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   "metadata": {},
   "source": [
    "### 85.将df1，df2，df3按照行合并为新DataFrame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
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       "      <th>25</th>\n",
       "      <td>25.000000</td>\n",
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       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>30.000000</td>\n",
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       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>35.000000</td>\n",
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       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>40.000000</td>\n",
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       "    <tr>\n",
       "      <th>29</th>\n",
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       "    <tr>\n",
       "      <th>30</th>\n",
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       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>55.000000</td>\n",
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       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>60.000000</td>\n",
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       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>65.000000</td>\n",
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       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>70.000000</td>\n",
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       "    <tr>\n",
       "      <th>35</th>\n",
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       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>80.000000</td>\n",
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       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>85.000000</td>\n",
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       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>90.000000</td>\n",
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       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>95.000000</td>\n",
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       "      <th>58</th>\n",
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  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 86.将df1，df2，df3按照列合并为新DataFrame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {
    "scrolled": true
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   "outputs": [
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       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>36</td>\n",
       "      <td>40</td>\n",
       "      <td>1.590059</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>66</td>\n",
       "      <td>45</td>\n",
       "      <td>-0.068341</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>24</td>\n",
       "      <td>50</td>\n",
       "      <td>-0.601103</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>9</td>\n",
       "      <td>55</td>\n",
       "      <td>0.798645</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>15</td>\n",
       "      <td>60</td>\n",
       "      <td>1.528142</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>22</td>\n",
       "      <td>65</td>\n",
       "      <td>-0.764393</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>57</td>\n",
       "      <td>70</td>\n",
       "      <td>0.642720</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>64</td>\n",
       "      <td>75</td>\n",
       "      <td>0.224422</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>54</td>\n",
       "      <td>80</td>\n",
       "      <td>-0.074785</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>73</td>\n",
       "      <td>85</td>\n",
       "      <td>0.214325</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>33</td>\n",
       "      <td>90</td>\n",
       "      <td>-0.078612</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>93</td>\n",
       "      <td>95</td>\n",
       "      <td>-0.677653</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     0   1         2\n",
       "0   93   0 -1.431807\n",
       "1   17   5 -0.417014\n",
       "2   80  10 -0.340834\n",
       "3   77  15 -0.522187\n",
       "4   57  20 -0.056280\n",
       "5   36  25 -1.166553\n",
       "6   11  30  0.185998\n",
       "7   98  35 -0.516752\n",
       "8   36  40  1.590059\n",
       "9   66  45 -0.068341\n",
       "10  24  50 -0.601103\n",
       "11   9  55  0.798645\n",
       "12  15  60  1.528142\n",
       "13  22  65 -0.764393\n",
       "14  57  70  0.642720\n",
       "15  64  75  0.224422\n",
       "16  54  80 -0.074785\n",
       "17  73  85  0.214325\n",
       "18  33  90 -0.078612\n",
       "19  93  95 -0.677653"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 87.查看df所有数据的最小值、25%分位数、中位数、75%分位数、最大值"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[-1.43180713  0.20724303 23.         61.         98.        ]\n"
     ]
    }
   ],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 88.修改列名为col1,col2,col3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 89.提取第一列中不在第二列出现的数字"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     93\n",
       "1     17\n",
       "3     77\n",
       "4     57\n",
       "5     36\n",
       "6     11\n",
       "7     98\n",
       "8     36\n",
       "9     66\n",
       "10    24\n",
       "11     9\n",
       "13    22\n",
       "14    57\n",
       "15    64\n",
       "16    54\n",
       "17    73\n",
       "18    33\n",
       "19    93\n",
       "Name: col1, dtype: int64"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 90.提取第一列和第二列出现频率最高的三个数字"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Int64Index([57, 80, 93], dtype='int64')"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 91.计算第一列数字前一个与后一个的差值"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[nan,\n",
       " -76.0,\n",
       " 63.0,\n",
       " -3.0,\n",
       " -20.0,\n",
       " -21.0,\n",
       " -25.0,\n",
       " 87.0,\n",
       " -62.0,\n",
       " 30.0,\n",
       " -42.0,\n",
       " -15.0,\n",
       " 6.0,\n",
       " 7.0,\n",
       " 35.0,\n",
       " 7.0,\n",
       " -10.0,\n",
       " 19.0,\n",
       " -40.0,\n",
       " 60.0]"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 92.将col1,col2,clo3三列顺序颠倒"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>col3</th>\n",
       "      <th>col2</th>\n",
       "      <th>col1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-1.431807</td>\n",
       "      <td>0</td>\n",
       "      <td>93</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-0.417014</td>\n",
       "      <td>5</td>\n",
       "      <td>17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-0.340834</td>\n",
       "      <td>10</td>\n",
       "      <td>80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-0.522187</td>\n",
       "      <td>15</td>\n",
       "      <td>77</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-0.056280</td>\n",
       "      <td>20</td>\n",
       "      <td>57</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>-1.166553</td>\n",
       "      <td>25</td>\n",
       "      <td>36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.185998</td>\n",
       "      <td>30</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>-0.516752</td>\n",
       "      <td>35</td>\n",
       "      <td>98</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>1.590059</td>\n",
       "      <td>40</td>\n",
       "      <td>36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>-0.068341</td>\n",
       "      <td>45</td>\n",
       "      <td>66</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>-0.601103</td>\n",
       "      <td>50</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>0.798645</td>\n",
       "      <td>55</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>1.528142</td>\n",
       "      <td>60</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>-0.764393</td>\n",
       "      <td>65</td>\n",
       "      <td>22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>0.642720</td>\n",
       "      <td>70</td>\n",
       "      <td>57</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>0.224422</td>\n",
       "      <td>75</td>\n",
       "      <td>64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>-0.074785</td>\n",
       "      <td>80</td>\n",
       "      <td>54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>0.214325</td>\n",
       "      <td>85</td>\n",
       "      <td>73</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>-0.078612</td>\n",
       "      <td>90</td>\n",
       "      <td>33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>-0.677653</td>\n",
       "      <td>95</td>\n",
       "      <td>93</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        col3  col2  col1\n",
       "0  -1.431807     0    93\n",
       "1  -0.417014     5    17\n",
       "2  -0.340834    10    80\n",
       "3  -0.522187    15    77\n",
       "4  -0.056280    20    57\n",
       "5  -1.166553    25    36\n",
       "6   0.185998    30    11\n",
       "7  -0.516752    35    98\n",
       "8   1.590059    40    36\n",
       "9  -0.068341    45    66\n",
       "10 -0.601103    50    24\n",
       "11  0.798645    55     9\n",
       "12  1.528142    60    15\n",
       "13 -0.764393    65    22\n",
       "14  0.642720    70    57\n",
       "15  0.224422    75    64\n",
       "16 -0.074785    80    54\n",
       "17  0.214325    85    73\n",
       "18 -0.078612    90    33\n",
       "19 -0.677653    95    93"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 93.提取第一列位置在1,10,15的数字"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1     17\n",
       "10    24\n",
       "15    64\n",
       "Name: col1, dtype: int64"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 94.查找第一列的局部最大值位置"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 2,  7,  9, 15, 17])"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#备注 即比它前一个与后一个数字的都大的数字\n",
    "tem = np.diff(np.sign(np.diff(df['col1'])))\n",
    "np.where(tem == -2)[0] + 1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 95. 按行计算df的每一行均值"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     30.522731\n",
       "1      7.194329\n",
       "2     29.886389\n",
       "3     30.492604\n",
       "4     25.647907\n",
       "5     19.944482\n",
       "6     13.728666\n",
       "7     44.161083\n",
       "8     25.863353\n",
       "9     36.977220\n",
       "10    24.466299\n",
       "11    21.599548\n",
       "12    25.509381\n",
       "13    28.745202\n",
       "14    42.547573\n",
       "15    46.408141\n",
       "16    44.641738\n",
       "17    52.738108\n",
       "18    40.973796\n",
       "19    62.440782\n",
       "dtype: float64"
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[['col1','col2','col3']].mean(axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 96.对第二列计算移动平均值"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 5., 10., 15., 20., 25., 30., 35., 40., 45., 50., 55., 60., 65.,\n",
       "       70., 75., 80., 85., 90.])"
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 97.将数据按照第三列值的大小升序排列"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 98.将第一列大于50的数字修改为'高'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  \"\"\"Entry point for launching an IPython kernel.\n"
     ]
    }
   ],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 99.计算第二列与第三列之间的欧式距离"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "248.26587436884793"
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 第五期 一些补充"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 101.从CSV文件中读取指定数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>positionName</th>\n",
       "      <th>salary</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>数据分析</td>\n",
       "      <td>37500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>数据建模</td>\n",
       "      <td>15000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>数据分析</td>\n",
       "      <td>3500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>数据分析</td>\n",
       "      <td>45000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>数据分析</td>\n",
       "      <td>30000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>数据分析</td>\n",
       "      <td>50000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>数据分析</td>\n",
       "      <td>30000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>数据建模工程师</td>\n",
       "      <td>35000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>数据分析专家</td>\n",
       "      <td>60000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>数据分析师</td>\n",
       "      <td>40000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  positionName  salary\n",
       "0         数据分析   37500\n",
       "1         数据建模   15000\n",
       "2         数据分析    3500\n",
       "3         数据分析   45000\n",
       "4         数据分析   30000\n",
       "5         数据分析   50000\n",
       "6         数据分析   30000\n",
       "7      数据建模工程师   35000\n",
       "8       数据分析专家   60000\n",
       "9        数据分析师   40000"
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": []
  }
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